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Zhao, Yueqin, Min Yi, and Ram C. Tiwari. "Extended likelihood ratio test-based methods for signal detection in a drug class with application to FDA’s adverse event reporting system database." Statistical Methods in Medical Research 27, no. 3 (May 2, 2016): 876–90. http://dx.doi.org/10.1177/0962280216646678.
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A likelihood ratio test, recently developed for the detection of signals of adverse events for a drug of interest in the FDA Adverse Events Reporting System database, is extended to detect signals of adverse events simultaneously for all the drugs in a drug class. The extended likelihood ratio test methods, based on Poisson model (Ext-LRT) and zero-inflated Poisson model (Ext-ZIP-LRT), are discussed and are analytically shown, like the likelihood ratio test method, to control the type-I error and false discovery rate. Simulation studies are performed to evaluate the performance characteristics of Ext-LRT and Ext-ZIP-LRT. The proposed methods are applied to the Gadolinium drug class in FAERS database. An in-house likelihood ratio test tool, incorporating the Ext-LRT methodology, is being developed in the Food and Drug Administration.
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Huang, Lan, Jyoti Zalkikar, and Ram Tiwari. "Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets." Computational and Mathematical Methods in Medicine 2019 (February 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/1526290.
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Pre- and postmarket drug safety evaluations usually include an integrated summary of results obtained using data from multiple studies related to a drug of interest. This paper proposes three approaches based on the likelihood ratio test (LRT), called the LRT methods, for drug safety signal detection from large observational databases with multiple studies, with focus on identifying signals of adverse events (AEs) from many AEs associated with a particular drug or inversely for signals of drugs associated with a particular AE. The methods discussed include simple pooled LRT method and its variations such as the weighted LRT that incorporates the total drug exposure information by study. The power and type-I error of the LRT methods are evaluated in a simulation study with varying heterogeneity across studies. For illustration purpose, these methods are applied to Proton Pump Inhibitors (PPIs) data with 6 studies for the effect of concomitant use of PPIs in treating patients with osteoporosis and to Lipiodol (a contrast agent) data with 13 studies for evaluating that drug’s safety profiles.
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Brum, Betania, Sidinei José Lopes, Daniel Furtado Ferreira, Lindolfo Storck, and Alberto Cargnelutti Filho. "Likelihood ratio test between two groups of castor oil plant traits." Ciência Rural 46, no. 7 (April 5, 2016): 1158–64. http://dx.doi.org/10.1590/0103-8478cr20151418.
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ABSTRACT: The likelihood ratio test (LRT), to the independence between two sets of variables, allows to identify whether there is a dependency relationship between them. The aim of this study was to calculate the type I error and power of the LRT for determining independence between two sets of variables under multivariate normal distributions in scenarios consisting of combinations of 16 sample sizes; 40 combinations of the number of variables of the two groups; and nine degrees of correlation between the variables (for the power). The rate of type I error and power were calculate at 640 and 5,760 scenarios, respectively. A performance evaluation of the LRT was conducted by computer simulation by the Monte Carlo method, using 2,000 simulations in each scenario. When the number of variables was large (24), the TRV controlled the rate of type I errors and showed high power in sizes greater than 100 samples. For small sample sizes (25, 30 and 50), the test showed good performance because the number of variables did not exceed 12.
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Chen, Huijun, and Tiefeng Jiang. "A study of two high-dimensional likelihood ratio tests under alternative hypotheses." Random Matrices: Theory and Applications 07, no. 01 (January 2018): 1750016. http://dx.doi.org/10.1142/s2010326317500162.
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Let [Formula: see text] be a [Formula: see text]-dimensional normal distribution. Testing [Formula: see text] equal to a given matrix or [Formula: see text] equal to a given pair through the likelihood ratio test (LRT) is a classical problem in the multivariate analysis. When the population dimension [Formula: see text] is fixed, it is known that the LRT statistics go to [Formula: see text]-distributions. When [Formula: see text] is large, simulation shows that the approximations are far from accurate. For the two LRT statistics, in the high-dimensional cases, we obtain their central limit theorems under a big class of alternative hypotheses. In particular, the alternative hypotheses are not local ones. We do not need the assumption that [Formula: see text] and [Formula: see text] are proportional to each other. The condition [Formula: see text] suffices in our results.
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Erickson, Stephen. "A Likelihood-Ratio Test of Twin Zygosity Using Molecular Genetic Markers." Twin Research and Human Genetics 11, no. 1 (February 1, 2008): 41–43. http://dx.doi.org/10.1375/twin.11.1.41.
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AbstractThe importance of using multiple polymorphic genetic markers to determine unambiguously whether a twin pair is monozygotic (MZ) or dizygotic (DZ) has long been recognized. Concordance among a set of markers is used as evidence of monozygosity, as it would be improbable for DZ twins to be concordant at a large number of polymorphic loci. Several sources give a formula for the probability of two DZ twins sharing the same genotype at a locus, assuming knowledge of allele frequencies but not of either twin's genotype; this probability can be used to determine whether a set of markers will reliably distinguish between MZ and DZ status in a randomly selected twin pair. If the shared genotype is known, however, the likelihood-ratio test (LRT) of the null hypothesis of dizygosity against the alternative hypothesis of monozygosity takes into account the observed genotype and, by the Neyman-Pearson lemma, is the most powerful test of its size. The LRT is equivalent to conditioning on the genotype of one of the twins, and computing the probability, assuming DZ status, of the other twin sharing that genotype. The resultingpvalues are frequently lower than those produced by the unconditional probability, especially if rare alleles are observed. The unconditional probability can be recapitulated from conditional probabilities by averaging across all of the conditioned sibling's possible genotypes. To illustrate properties of the LRT applied to multiple markers, the probability distribution of the LRTpvalue is computed from allele frequencies of twelve unlinked markers published in Elbaz et al. (2006) and compared with thepvalue computed from unconditional probabilities.
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Lee, Sunbok. "Detecting Differential Item Functioning Using the Logistic Regression Procedure in Small Samples." Applied Psychological Measurement 41, no. 1 (September 24, 2016): 30–43. http://dx.doi.org/10.1177/0146621616668015.
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The logistic regression (LR) procedure for testing differential item functioning (DIF) typically depends on the asymptotic sampling distributions. The likelihood ratio test (LRT) usually relies on the asymptotic chi-square distribution. Also, the Wald test is typically based on the asymptotic normality of the maximum likelihood (ML) estimation, and the Wald statistic is tested using the asymptotic chi-square distribution. However, in small samples, the asymptotic assumptions may not work well. The penalized maximum likelihood (PML) estimation removes the first-order finite sample bias from the ML estimation, and the bootstrap method constructs the empirical sampling distribution. This study compares the performances of the LR procedures based on the LRT, Wald test, penalized likelihood ratio test (PLRT), and bootstrap likelihood ratio test (BLRT) in terms of the statistical power and type I error for testing uniform and non-uniform DIF. The result of the simulation study shows that the LRT with the asymptotic chi-square distribution works well even in small samples.
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Zhang, Yangsong, Li Dong, Rui Zhang, Dezhong Yao, Yu Zhang, and Peng Xu. "An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/908719.
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An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.
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Kawasaki, Tamae, and Takashi Seo. "Modified Likelihood Ratio Test for Sub-mean Vectors with Two-step Monotone Missing Data in Two-sample Problem." Austrian Journal of Statistics 50, no. 1 (February 3, 2021): 88–104. http://dx.doi.org/10.17713/ajs.v50i1.928.
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This article deals with the problem of testing for two normal sub-mean vectors when the data set have two-step monotone missing observations. Under the assumptions that the population covariance matrices are equal, we obtain the likelihood ratio test (LRT) statistic. Furthermore, an asymptotic expansion for the null distribution of the LRT statistic is derived under the two-step monotone missing data by the perturbation method. Using the result, we propose two improved statistics with good chi-squared approximation. One is the modified LRT statistic by Bartlett correction,and the other is the modified LRT statistic using the modification coefficient by linear interpolation. The accuracy of the approximations are investigated by using a Monte Carlo simulation. The proposed methods are illustrated using an example.
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Kim, Seon Man. "Auditory Device Voice Activity Detection Based on Statistical Likelihood-Ratio Order Statistics." Applied Sciences 10, no. 15 (July 22, 2020): 5026. http://dx.doi.org/10.3390/app10155026.
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This paper proposes a technique for improving statistical-model-based voice activity detection (VAD) in noisy environments to be applied in an auditory hearing aid. The proposed method is implemented for a uniform polyphase discrete Fourier transform filter bank satisfying an auditory device time latency of 8 ms. The proposed VAD technique provides an online unified framework to overcome the frequent false rejection of the statistical-model-based likelihood-ratio test (LRT) in noisy environments. The method is based on the observation that the sparseness of speech and background noise cause high false-rejection error rates in statistical LRT-based VAD—the false rejection rate increases as the sparseness increases. We demonstrate that the false-rejection error rate can be reduced by incorporating likelihood-ratio order statistics into a conventional LRT VAD. We confirm experimentally that the proposed method relatively reduces the average detection error rate by 15.8% compared to a conventional VAD with only minimal change in the false acceptance probability for three different noise conditions whose signal-to-noise ratio ranges from 0 to 20 dB.
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Zhao, Hongbo, Lei Chen, Wenquan Feng, and Chuan Lei. "A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/8535486.
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Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.
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Wasserman, Larry, Aaditya Ramdas, and Sivaraman Balakrishnan. "Universal inference." Proceedings of the National Academy of Sciences 117, no. 29 (July 6, 2020): 16880–90. http://dx.doi.org/10.1073/pnas.1922664117.
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We propose a general method for constructing confidence sets and hypothesis tests that have finite-sample guarantees without regularity conditions. We refer to such procedures as “universal.” The method is very simple and is based on a modified version of the usual likelihood-ratio statistic that we call “the split likelihood-ratio test” (split LRT) statistic. The (limiting) null distribution of the classical likelihood-ratio statistic is often intractable when used to test composite null hypotheses in irregular statistical models. Our method is especially appealing for statistical inference in these complex setups. The method we suggest works for any parametric model and also for some nonparametric models, as long as computing a maximum-likelihood estimator (MLE) is feasible under the null. Canonical examples arise in mixture modeling and shape-constrained inference, for which constructing tests and confidence sets has been notoriously difficult. We also develop various extensions of our basic methods. We show that in settings when computing the MLE is hard, for the purpose of constructing valid tests and intervals, it is sufficient to upper bound the maximum likelihood. We investigate some conditions under which our methods yield valid inferences under model misspecification. Further, the split LRT can be used with profile likelihoods to deal with nuisance parameters, and it can also be run sequentially to yield anytime-valid P values and confidence sequences. Finally, when combined with the method of sieves, it can be used to perform model selection with nested model classes.
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Bustamante, Carlos D., John Wakeley, Stanley Sawyer, and Daniel L. Hartl. "Directional Selection and the Site-Frequency Spectrum." Genetics 159, no. 4 (December 1, 2001): 1779–88. http://dx.doi.org/10.1093/genetics/159.4.1779.
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Abstract In this article we explore statistical properties of the maximum-likelihood estimates (MLEs) of the selection and mutation parameters in a Poisson random field population genetics model of directional selection at DNA sites. We derive the asymptotic variances and covariance of the MLEs and explore the power of the likelihood ratio tests (LRT) of neutrality for varying levels of mutation and selection as well as the robustness of the LRT to deviations from the assumption of free recombination among sites. We also discuss the coverage of confidence intervals on the basis of two standard-likelihood methods. We find that the LRT has high power to detect deviations from neutrality and that the maximum-likelihood estimation performs very well when the ancestral states of all mutations in the sample are known. When the ancestral states are not known, the test has high power to detect deviations from neutrality for negative selection but not for positive selection. We also find that the LRT is not robust to deviations from the assumption of independence among sites.
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Minică, Camelia C., Giulio Genovese, Christina M. Hultman, René Pool, Jacqueline M. Vink, Michael C. Neale, Conor V. Dolan, and Benjamin M. Neale. "The Weighting is the Hardest Part: On the Behavior of the Likelihood Ratio Test and the Score Test Under a Data-Driven Weighting Scheme in Sequenced Samples." Twin Research and Human Genetics 20, no. 2 (February 27, 2017): 108–18. http://dx.doi.org/10.1017/thg.2017.7.
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Sequence-based association studies are at a critical inflexion point with the increasing availability of exome-sequencing data. A popular test of association is the sequence kernel association test (SKAT). Weights are embedded within SKAT to reflect the hypothesized contribution of the variants to the trait variance. Because the true weights are generally unknown, and so are subject to misspecification, we examined the efficiency of a data-driven weighting scheme. We propose the use of a set of theoretically defensible weighting schemes, of which, we assume, the one that gives the largest test statistic is likely to capture best the allele frequency–functional effect relationship. We show that the use of alternative weights obviates the need to impose arbitrary frequency thresholds. As both the score test and the likelihood ratio test (LRT) may be used in this context, and may differ in power, we characterize the behavior of both tests. The two tests have equal power, if the weights in the set included weights resembling the correct ones. However, if the weights are badly specified, the LRT shows superior power (due to its robustness to misspecification). With this data-driven weighting procedure the LRT detected significant signal in genes located in regions already confirmed as associated with schizophrenia — the PRRC2A (p = 1.020e-06) and the VARS2 (p = 2.383e-06) — in the Swedish schizophrenia case-control cohort of 11,040 individuals with exome-sequencing data. The score test is currently preferred for its computational efficiency and power. Indeed, assuming correct specification, in some circumstances, the score test is the most powerful test. However, LRT has the advantageous properties of being generally more robust and more powerful under weight misspecification. This is an important result given that, arguably, misspecified models are likely to be the rule rather than the exception in weighting-based approaches.
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GÓRRIZ, J. M., C. G. PUNTONET, and J. RAMÍREZ. "TESTING FOR INDEPENDENCE AND LRT FOR VAD." International Journal of Information Acquisition 02, no. 04 (December 2005): 315–22. http://dx.doi.org/10.1142/s0219878905000672.
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In this paper we propose a simple method for Voice Activity Detection (VAD) in noisy environments based on periodogram of squares. The approach is based on a test for independence between a sequence of identically distributed random variables using the classical technique of Likelihood Ratio Test (LRT). This algorithm differs from many others in the way the decision rule is formulated (detection tests) and the domain used in this approach. Clear improvements in the speed of speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It has been shown that application of statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The experimental analysis carried out on the AURORA databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs based in statistical tests.
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Anisimova, Maria, Rasmus Nielsen, and Ziheng Yang. "Effect of Recombination on the Accuracy of the Likelihood Method for Detecting Positive Selection at Amino Acid Sites." Genetics 164, no. 3 (July 1, 2003): 1229–36. http://dx.doi.org/10.1093/genetics/164.3.1229.
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AbstractMaximum-likelihood methods based on models of codon substitution accounting for heterogeneous selective pressures across sites have proved to be powerful in detecting positive selection in protein-coding DNA sequences. Those methods are phylogeny based and do not account for the effects of recombination. When recombination occurs, such as in population data, no unique tree topology can describe the evolutionary history of the whole sequence. This violation of assumptions raises serious concerns about the likelihood method for detecting positive selection. Here we use computer simulation to evaluate the reliability of the likelihood-ratio test (LRT) for positive selection in the presence of recombination. We examine three tests based on different models of variable selective pressures among sites. Sequences are simulated using a coalescent model with recombination and analyzed using codon-based likelihood models ignoring recombination. We find that the LRT is robust to low levels of recombination (with fewer than three recombination events in the history of a sample of 10 sequences). However, at higher levels of recombination, the type I error rate can be as high as 90%, especially when the null model in the LRT is unrealistic, and the test often mistakes recombination as evidence for positive selection. The test that compares the more realistic models M7 (β) against M8 (β and ω) is more robust to recombination, where the null model M7 allows the positive selection pressure to vary between 0 and 1 (and so does not account for positive selection), and the alternative model M8 allows an additional discrete class with ω= dN/dS that could be estimated to be >1 (and thus accounts for positive selection). Identification of sites under positive selection by the empirical Bayes method appears to be less affected than the LRT by recombination.
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Suyitno, Suyitno. "Penaksiran Parameter dan Pengujian Hipotesis Model Regresi Weibull Univariat." JURNAL EKSPONENSIAL 8, no. 2 (December 21, 2017): 179. http://dx.doi.org/10.30872/eksponensial.v8i2.41.
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In this study, a univariate Weibull regression model is discussed. The Weibull regression is a regression model developed from the Weibull distribution, that is the Weibull distribution depending on the covariates or the regression parameters. The univariate Weibull regression (UWR) model can involve the survival function model and the mean model of the response variable with the scale parameter stated in the terms of the regression parameters. The aim of this study is to estimate the UWR model parameters using the maximum likelihood estimation (MLE) method, and to test the regression parameters. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. The regression parameters testing involves simultaneous and partial test. The test statistic for simultaneous test is Wilk's likelihood ratio. Wilk statistic follows Chi-square distribution, which can be derived from the likelihood ratio test (LRT) method. The test statistic for partial test is Wald and it follows standard normal distribution. The alternative test statistik for partial test is squared of Wald statistic, where it follows Chi-square distribution with one degree of freedom.
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Li, Teng. "Improved Tests for an Ordered Hypothesis in one Parameter Exponential Families." Calcutta Statistical Association Bulletin 43, no. 1-2 (March 1993): 57–64. http://dx.doi.org/10.1177/0008068319930105.
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We consider m independent one parameter exponential families with parameters (θ1, θ2, , θ m), and the alternative hypothesis [Formula: see text] where [Formula: see text] are specified. The null hypothesis Ho is the complement of H1. A class of tests more powerful than the likelihood ratio test (LRT) is derived. Applications to two special cases, Binomial and Poisson, are indicated. AMS 1980 Subject Classification: Primary 62F03
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Lieberman, Offer, Judith Rousseau, and David M. Zucker. "SMALL-SAMPLE LIKELIHOOD-BASED INFERENCE IN THE ARFIMA MODEL." Econometric Theory 16, no. 2 (April 2000): 231–48. http://dx.doi.org/10.1017/s0266466600162048.
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The autoregressive fractionally integrated moving average (ARFIMA) model has become a popular approach for analyzing time series that exhibit long-range dependence. For the Gaussian case, there have been substantial advances in the area of likelihood-based inference, including development of the asymptotic properties of the maximum likelihood estimates and formulation of procedures for their computation. Small-sample inference, however, has not to date been studied. Here we investigate the small-sample behavior of the conventional and Bartlett-corrected likelihood ratio tests (LRT) for the fractional difference parameter. We derive an expression for the Bartlett correction factor. We investigate the asymptotic order of approximation of the Bartlett-corrected test. In addition, we present a small simulation study of the conventional and Bartlett-corrected LRT's. We find that for simple ARFIMA models both tests perform fairly well with a sample size of 40 but the Bartlett-corrected test generally provides an improvement over the conventional test with a sample size of 20.
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Sur, Pragya, and Emmanuel J. Candès. "A modern maximum-likelihood theory for high-dimensional logistic regression." Proceedings of the National Academy of Sciences 116, no. 29 (July 1, 2019): 14516–25. http://dx.doi.org/10.1073/pnas.1810420116.
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Students in statistics or data science usually learn early on that when the sample size n is large relative to the number of variables p, fitting a logistic model by the method of maximum likelihood produces estimates that are consistent and that there are well-known formulas that quantify the variability of these estimates which are used for the purpose of statistical inference. We are often told that these calculations are approximately valid if we have 5 to 10 observations per unknown parameter. This paper shows that this is far from the case, and consequently, inferences produced by common software packages are often unreliable. Consider a logistic model with independent features in which n and p become increasingly large in a fixed ratio. We prove that (i) the maximum-likelihood estimate (MLE) is biased, (ii) the variability of the MLE is far greater than classically estimated, and (iii) the likelihood-ratio test (LRT) is not distributed as a χ2. The bias of the MLE yields wrong predictions for the probability of a case based on observed values of the covariates. We present a theory, which provides explicit expressions for the asymptotic bias and variance of the MLE and the asymptotic distribution of the LRT. We empirically demonstrate that these results are accurate in finite samples. Our results depend only on a single measure of signal strength, which leads to concrete proposals for obtaining accurate inference in finite samples through the estimate of this measure.
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Cho, Sun-Joo, Paul De Boeck, and Woo-Yeol Lee. "Evaluating Testing, Profile Likelihood Confidence Interval Estimation, and Model Comparisons for Item Covariate Effects in Linear Logistic Test Models." Applied Psychological Measurement 41, no. 5 (February 1, 2017): 353–71. http://dx.doi.org/10.1177/0146621617692078.
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The linear logistic test model (LLTM) has been widely applied to investigate the effects of item covariates on item difficulty. The LLTM was extended with random item residuals to account for item differences not explained by the item covariates. This extended LLTM is called the LLTM-R. In this article, statistical inference methods are investigated for these two models. Type I error rates and power are compared via Monte Carlo studies. Based on the simulation results, the use of the likelihood ratio test (LRT) is recommended over the paired-sample t test based on sum scores, the Wald z test, and information criteria, and the LRT is recommended over the profile likelihood confidence interval because of the simplicity of the LRT. In addition, it is concluded that the LLTM-R is the better general model approach. Inferences based on the LLTM while the LLTM-R is the true model appear to be largely biased in the liberal way, while inferences based on the LLTM-R while the LLTM is the true model are only biased in a very minor and conservative way. Furthermore, in the absence of residual variance, Type I error rate and power were acceptable except for power when the number of items is small (10 items) and also the number of persons is small (200 persons). In the presence of residual variance, however, the number of items needs to be large (80 items) to avoid an inflated Type I error and to reach a power level of .90 for a moderate effect.
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Juskowiak, Jochen, and Bernd Bertsche. "Application and Simulation Study of Stress-Dependent Weibull Lifetime Models." International Journal of Reliability, Quality and Safety Engineering 23, no. 02 (April 2016): 1650008. http://dx.doi.org/10.1142/s021853931650008x.
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Different Weibull lifetime models are presented whose scale, shape and minimum lifetime parameters are stress-dependent. This allows describing and predicting the lifetime of products with a Weibull distribution more accurately wherever stress-dependence applies to the failure mechanism. For instance, this is the case for failures due to fatigue, on which this paper focusses. The proposed procedure encompasses a two-step maximum likelihood estimation and a Fisher matrix (FM) confidence bounds calculation, followed by a model evaluation. This model evaluation is conducted by means of a general plausibility check (PC), likelihood ratio test (LRT) and Bayesian information criterion (BIC). Their applicability to accelerated life test data is discussed and validated using test data. Finally, a simulation study confirms a wide range of applicability.
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Song, Chong, Bingnan Wang, Maosheng Xiang, and Wei Li. "Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector." Sensors 21, no. 4 (February 20, 2021): 1478. http://dx.doi.org/10.3390/s21041478.
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A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.
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Zhao, Yipin, Zebin Lin, Yingying Ji, Huawei Wang, Li Xiao, Qingwei Chen, and Zhiqin Wu. "Gamma-Glutamyl Transpeptidase to Platelet Ratio: A New Inflammatory Marker Associated with Outcomes after Cardiac Arrest." Mediators of Inflammation 2021 (August 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/5537966.
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Introduction. In recent years, gamma-glutamyl transpeptidase to platelet ratio (GPR) has been proposed as a new inflammatory marker. We aimed to evaluate the association between GPR and outcomes after cardiac arrest (CA). Methods. A total of 354 consecutive patients with CA were included in this retrospective study. Patients were divided into three groups according to tertiles of GPR (low, n = 119 ; middle, n = 117 ; and high, n = 118 ). To determine the relationship between GPR and prognosis, a logistic regression analysis was performed. The ability of GPR to predict the outcomes was evaluated by receiver operating characteristic (ROC) curve analysis. Two prediction models were established, and the likelihood ratio test (LRT) and the Akaike Information Criterion (AIC) were utilized for model comparison. Results. Among the 354 patients (age 62 [52, 74], 254/354 male) who were finally included in the analysis, those in the high GPR group had poor outcomes. Multivariate logistic regression analysis revealed that GPR was independently associated with the three outcomes, for ICU mortality ( odds ratios OR = 1.738 , 95% confidence interval (CI): 1.221-2.474, P = 0.002 ), hospital mortality ( OR = 1.676 1.164 − 2.413 , P = 0.005 ), and unfavorable neurologic outcomes ( OR = 1.623 1.121 − 2.351 , P = 0.010 ). The area under the ROC curve was 0.611 (95% Cl: 0.558-0.662) for ICU mortality, 0.600 (95% CI: 0.547-0.651) for hospital mortality, and 0.602 (95% CI: 0.549-0.653) for unfavorable neurologic outcomes. Further, the LRT analysis showed that compared with the model without GPR, the GPR-combined model had a higher likelihood ratio χ 2 score and smaller AIC. Conclusion. GPR, as an inflammatory indicator, was independently associated with outcomes after CA. GPR is helpful in estimating the clinical outcomes of patients with CA.
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Al-Moisheer, A. S. "Sequential Test for a Mixture of Finite Exponential Distribution." Journal of Mathematics 2021 (April 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625853.
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Testing the number of components in a finite mixture is considered one of the challenging problems. In this paper, exponential finite mixtures are used to determine the number of components in a finite mixture. A sequential testing procedure is adopted based on the likelihood ratio test (LRT) statistic. The distribution of the test statistic under the null hypothesis is obtained using a resampling technique based on B bootstrap samples. The quantiles of the distribution of the test statistic are evaluated from the B bootstrap samples. The performance of the test is examined through the empirical power and application on two real datasets. The proposed procedure is not only used for testing the number of components but also for estimating the optimal number of components in a finite exponential mixture distribution. The innovation of this paper is the sequential test, which tests the more general hypothesis of a finite exponential mixture of k components versus a mixture of k + 1 components. The special case of testing an exponential mixture of one component versus two components is the one commonly used in the literature.
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KIM, JONGJOO, SCOTT K. DAVIS, and JEREMY F. TAYLOR. "Application of non-parametric bootstrap methods to estimate confidence intervals for QTL location in a beef cattle QTL experimental population." Genetical Research 79, no. 3 (June 2002): 259–63. http://dx.doi.org/10.1017/s001667230200561x.
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Empirical confidence intervals (CIs) for the estimated quantitative trait locus (QTL) location from selective and non-selective non-parametric bootstrap resampling methods were compared for a genome scan involving an Angus×Brahman reciprocal fullsib backcross population. Genetic maps, based on 357 microsatellite markers, were constructed for 29 chromosomes using CRI-MAP V2.4. Twelve growth, carcass composition and beef quality traits (n = 527–602) were analysed to detect QTLs utilizing (composite) interval mapping approaches. CIs were investigated for 28 likelihood ratio test statistic (LRT) profiles for the one QTL per chromosome model. The CIs from the non-selective bootstrap method were largest (87·7 cM average or 79·2% coverage of test chromosomes). The Selective II procedure produced the smallest CI size (42·3 cM average). However, CI sizes from the Selective II procedure were more variable than those produced by the two LOD drop method. CI ranges from the Selective II procedure were also asymmetrical (relative to the most likely QTL position) due to the bias caused by the tendency for the estimated QTL position to be at a marker position in the bootstrap samples and due to monotonicity and asymmetry of the LRT curve in the original sample.
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Park, Goeun, Heesun Jung, Seok-Jae Heo, and Inkyung Jung. "Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance." Life 10, no. 8 (August 5, 2020): 138. http://dx.doi.org/10.3390/life10080138.
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There are several different proposed data mining methods for the postmarketing surveillance of drug safety. Adverse events are often classified into a hierarchical structure. Our objective was to compare the performance of several of these different data mining methods for adverse drug events data with a hierarchical structure. We generated datasets based on the World Health Organization’s Adverse Reaction Terminology (WHO-ART) hierarchical structure. We evaluated different data mining methods for signal detection, including several frequentist methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), the likelihood ratio test-based method (LRT), and Bayesian methods such as gamma Poisson shrinker (GPS), Bayesian confidence propagating neural network (BCPNN), the new IC method, and the simplified Bayesian method (sB), as well as the tree-based scan statistic through an extensive simulation study. We also applied the methods to real data on two diabetes drugs, voglibose and acarbose, from the Korea Adverse event reporting system. Only the tree-based scan statistic method maintained the type I error rate at the desired level. Likelihood ratio test-based methods and Bayesian methods tended to be more conservative than other methods in the simulation study and detected fewer signals in the real data example. No method was superior to the others in terms of the statistical power and sensitivity of detecting true signals. It is recommended that those conducting drug‒adverse event surveillance use not just one method, but make a decision based on several methods.
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Luo, Bing, Zheng Pei, Li Xu, and Da Li Hu. "A New Method Based on HMMs and K-Means Algorithms for Noise-Robust Voice Activity Detector." Applied Mechanics and Materials 128-129 (October 2011): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.461.
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In this paper, we proposed left-right hidden Markov models (HMMs) combination with k-means threshold of Likelihood ratio test (LRT) to identify the start and end of the speech. This method builds two models of non-speech and speech but not two states, i.e. each model could conclude several states. In the experiments we present the Voice Activity Detection (VAD) results between two states hidden semi-Markov model (HSMM) and proposed algorithm. We also compare accuracy and robust between the k-means threshold and the adaptive threshold in high signal to noise rate in the background noise. It presents that k-means threshold is more effective than the adaptive threshold and the proposed method also make a better performance than two states HSMM based VAD, especially in the low signal-to-noise ratio (SNR) environment.
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WANG, JIANKANG, XIANGYUAN WAN, JOSE CROSSA, JONATHAN CROUCH, JIANFENG WENG, HUQU ZHAI, and JIANMIN WAN. "QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines." Genetical Research 88, no. 2 (October 2006): 93–104. http://dx.doi.org/10.1017/s0016672306008408.
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Chromosome segment substitution (CSS) lines have the potential for use in QTL fine mapping and map-based cloning. The standard t-test used in the idealized case that each CSS line has a single segment from the donor parent is not suitable for non-idealized CSS lines carrying several substituted segments from the donor parent. In this study, we present a likelihood ratio test based on stepwise regression (RSTEP-LRT) that can be used for QTL mapping in a population consisting of non-idealized CSS lines. Stepwise regression is used to select the most important segments for the trait of interest, and the likelihood ratio test is used to calculate the LOD score of each chromosome segment. This method is statistically equivalent to the standard t-test with idealized CSS lines. To further improve the power of QTL mapping, a method is proposed to decrease multicollinearity among markers (or chromosome segments). QTL mapping with an example CSS population in rice consisting of 65 non-idealized CSS lines and 82 chromosome segments indicated that a total of 18 segments on eight of the 12 rice chromosomes harboured QTLs affecting grain length under the LOD threshold of 2·5. Three major stable QTLs were detected in all eight environments. Some minor QTLs were not detected in all environments, but they could increase or decrease the grain length constantly. These minor genes are also useful in marker-assisted gene pyramiding.
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Lu, Zhengri, Genshan Ma, and Lijuan Chen. "De-Ritis Ratio Is Associated with Mortality after Cardiac Arrest." Disease Markers 2020 (November 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/8826318.
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Introduction. The aim of our study was to explore the associations of the aspartate transaminase/alanine transaminase (De-Ritis) ratio with outcomes after cardiac arrest (CA). Methods. This retrospective study included 374 consecutive adult cardiac arrest patients. Information on the study population was obtained from the Dryad Digital Repository. Patients were divided into tertiles based on their De-Ritis ratio. The logistic regression hazard analysis was used to assess the independent relationship between the De-Ritis ratio and mortality. The Kaplan-Meier method and log-rank test were used to estimate the survival of different groups. Receiver operating characteristic (ROC) curve analysis was utilized to compare the prognostic ability of biomarkers. A model combining the De-Ritis ratio was established, and its performance was evaluated using the Akaike information criterion (AIC). Results. Of the 374 patients who were included in the study, 194 patients (51.9%) died in the intensive care unit (ICU), 213 patients (57.0%) died during hospitalization, and 226 patients (60.4%) had an unfavorable neurologic outcome. Logistic regression analysis including potentially confounding factors showed that the De-Ritis ratio was independently associated with mortality, yielding a more than onefold risk of ICU mortality (OR 1.455; 95% CI 1.088-1.946; p = 0.011 ) and hospital mortality (OR 1.378; 95% CI 1.031-1.842; p = 0.030 ). Discriminatory performance assessed by ROC curves showed an area under the curve of 0.611 (95% CI 0.553-0.668) for ICU mortality and 0.625 (0.567-0.682) for hospital mortality. Further, the likelihood ratio test (LRT) analysis showed that the model combining the De-Ritis ratio had a smaller AIC and higher likelihood ratio χ 2 score than the model without the De-Ritis ratio. The Kaplan-Meier curves showed that the CA patients in the De-Ritis ratio tertile 3 group clearly had a significantly higher incidence of ICU mortality ( log − rank = 0.007 ). Conclusion. An elevated De-Ritis ratio on admission was significantly associated with ICU mortality and hospital mortality after CA. Assessment of the De-Ritis ratio might help identify groups at high risk for mortality.
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García-Otero, Mariano, and Adrián Población-Hernández. "Location Aided Cooperative Detection of Primary User Emulation Attacks in Cognitive Wireless Sensor Networks Using Nonparametric Techniques." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9571592.
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Primary user emulation (PUE) attacks are a major security challenge to cognitive wireless sensor networks (CWSNs). In this paper, we propose two variants of the PUE attack, namely, the relay and replay attacks. Such threats are conducted by malicious nodes that replicate the transmissions of a real primary user (PU), thus making them resilient to many defensive procedures. However, we show that those PUE attacks can be effectively detected by a set of cooperating secondary users (SUs), using location information and received signal strength (RSS) measurements. Two strategies for the detection of PUE relay and replay attacks are presented in the paper: parametric and nonparametric. The parametric scheme is based on the likelihood ratio test (LRT) and requires the existence of a precise path loss model for the observed RSS values. On the contrary, the nonparametric procedure is not tied to any particular propagation model; so, it does not require any calibration process and is robust to changing environmental conditions. Simulations show that the nonparametric detection approach is comparable in performance to the LRT under moderate shadowing conditions, specially in case of replay attacks.
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ESPOSITO, ANNA, VOJTĚCH STEJSKAL, and ZDENĚK SMÉKAL. "COGNITIVE ROLE OF SPEECH PAUSES AND ALGORITHMIC CONSIDERATIONS FOR THEIR PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 1073–88. http://dx.doi.org/10.1142/s0218001408006508.
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This study investigates pausing strategies, focusing the attention on empty speech pauses. A cross-modal analysis (video and audio) of spontaneous narratives produced by male and female children and adults showed that a remarkable amount of empty speech pauses was used to signal new concepts in the speech flow and to segment discourse units such as clauses and paragraphs. Based on these results, an adaptive mathematical model for pause distribution was suggested, that exploits, as pause features, the absence of signal and/or the changes of energy over different acoustic dimensions strongly related to the auditory perception. These considerations inspired the formulation and the implementation of two pause detection procedures that proved to be more effective than the Likelihood Ratio Test (LRT) and Long-Term Spectral Divergence (LTSD) algorithms recently proposed in literature and applied for Voice Activity Detection (VAD).
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Kao, Chen-Hung. "On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci." Genetics 156, no. 2 (October 1, 2000): 855–65. http://dx.doi.org/10.1093/genetics/156.2.855.
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AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.
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Madad, Mostafa, Navid Ghavi Hossein-Zadeh, and Abdol Ahad Shadparvar. "Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo." Pesquisa Agropecuária Brasileira 51, no. 7 (July 2016): 890–97. http://dx.doi.org/10.1590/s0100-204x2016000700012.
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Abstract: The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials (LPs). A total of 2,538 test-day milk yield (TDMY) records from 516 first lactation records of Khuzestan buffalo, calving from 1993 to 2009 and belonging to 150 herds located in the state of Khuzestan, Iran, were analyzed. The residual variances were modeled through a step function with 1, 5, 6, 9, and 19 classes. The additive genetic and permanent environmental random effects were modeled by LPs of days in milk using quadratic to septic polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by cubic and third order LP, respectively, and with the residual variance modeled through a step function with nine classes was the most adequate one to describe the covariance structure. The model with the highest significant log-likelihood ratio test (LRT) and with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) was considered to be the most appropriate one. Unexpected negative genetic correlation estimates were obtained between TDMY records of the twenty-fifth and thirty-seventh week (-0.03). Genetic correlation estimates were generally higher, close to unity, between adjacent weeks during the middle of lactation. Random regression models can be used for routine genetic evaluation of milk yield in Khuzestan buffalo.
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Ferreira Coelho, Igor, Marco Antônio Peixoto, Jeniffer Santana Pinto Coelho Evangelista, Rodrigo Silva Alves, Suellen Sales, Marcos Deon Vilela de Resende, Jefferson Fernando Naves Pinto, Edésio Fialho dos Reis, and Leonardo Lopes Bhering. "Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding." PLOS ONE 15, no. 11 (November 20, 2020): e0242705. http://dx.doi.org/10.1371/journal.pone.0242705.
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An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.
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Denos, Marion, Xiao-Mei Mai, Bjørn Olav Åsvold, Elin Pettersen Sørgjerd, Yue Chen, and Yi-Qian Sun. "Vitamin D status and risk of type 2 diabetes in the Norwegian HUNT cohort study: does family history or genetic predisposition modify the association?" BMJ Open Diabetes Research & Care 9, no. 1 (January 2021): e001948. http://dx.doi.org/10.1136/bmjdrc-2020-001948.
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IntroductionWe sought to investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) level and the risk of type 2 diabetes mellitus (T2DM) in adults who participated in the Trøndelag Health Study (HUNT), and the possible effect modification by family history and genetic predisposition.Research design and methodsThis prospective study included 3574 diabetes-free adults at baseline who participated in the HUNT2 (1995–1997) and HUNT3 (2006–2008) surveys. Serum 25(OH)D levels were determined at baseline and classified as <50 and ≥50 nmol/L. Family history of diabetes was defined as self-reported diabetes among parents and siblings. A Polygenic Risk Score (PRS) for T2DM based on 166 single-nucleotide polymorphisms was generated. Incident T2DM was defined by self-report and/or non-fasting glucose levels greater than 11 mmol/L and serum glutamic acid decarboxylase antibody level of <0.08 antibody index at the follow-up. Multivariable logistic regression models were applied to calculate adjusted ORs with 95% CIs. Effect modification by family history or PRS was assessed by likelihood ratio test (LRT).ResultsOver 11 years of follow-up, 92 (2.6%) participants developed T2DM. A higher risk of incident T2DM was observed in participants with serum 25(OH)D level of<50 nmol/L compared with those of ≥50 nmol/L (OR 1.72, 95% CI 1.03 to 2.86). Level of 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in adults without family history of diabetes (OR 3.87, 95% CI 1.62 to 9.24) but not in those with a family history (OR 0.72, 95% CI 0.32 to 1.62, p value for LRT=0.003). There was no effect modification by PRS (p value for LRT>0.23).ConclusionSerum 25(OH)D<50 nmol/L was associated with an increased risk of T2DM in Norwegian adults. The inverse association was modified by family history of diabetes but not by genetic predisposition to T2DM.
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Wang, Wenwen, Xinyun Chen, Weisheng Zeng, Jianjun Wang, and Jinghui Meng. "Development of a Mixed-Effects Individual-Tree Basal Area Increment Model for Oaks (Quercus spp.) Considering Forest Structural Diversity." Forests 10, no. 6 (May 30, 2019): 474. http://dx.doi.org/10.3390/f10060474.
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In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (Quercus spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.
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Tamioso, Priscilla Regina, Jaime Luiz Alberti Filho, Laila Talarico Dias, and Rodrigo de Almeida Teixeira. "Estimates of (co)variance components and genetic parameters for growth traits in Suffolk lambs." Ciência Rural 43, no. 12 (December 2013): 2215–20. http://dx.doi.org/10.1590/s0103-84782013001200016.
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The study aimed to estimate the components of (co)variance and heritability for weights at birth (BW), weaning (WW) and 180 days of age (W180), as well as the average daily gains from birth to weaning (ADG1), birth to 180 days of age (ADG2) and weaning to 180 days of age (ADG3) in Suffolk sheep. Thus, three different single-trait animal models were fitted, considering the direct additive genetic effect (Model 1), the direct additive genetic and maternal permanent environmental effects (Model 2), and in Model 3, in addition to those in Model 2, the maternal additive genetic effect was included. After comparing models through the likelihood ratio test (LRT), model 3 was chosen as the most appropriate to estimate heritability for BW, WW and ADG1. Model 2 was considered as the best to estimate the coefficient of heritability for W180 and ADG2, and model 1 for ADG3. Direct heritability estimates were inflated when maternal effects were ignored. According to the most suitable models, the heritability estimates for BW, WW, W180, ADG1, ADG2 and ADG3 were 0.06, 0.08, 0.09, 0.07, 0.08 and 0.07, respectively, indicating low possibility of genetic gain through individual selection. The results show the importance of including maternal effects in the models to properly estimate genetic parameters even at post-weaning ages.
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Foroutan, Hourieh, Amirhossein Amiri, and Reza Kamranrad. "Improving Phase I Monitoring of Dirichlet Regression Profiles." International Journal of Reliability, Quality and Safety Engineering 25, no. 06 (December 2018): 1850030. http://dx.doi.org/10.1142/s0218539318500304.
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In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.
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Koh, C. K. H., J. Shi, W. J. Williams, and J. Ni. "Multiple Fault Detection and Isolation Using the Haar Transform, Part 1: Theory." Journal of Manufacturing Science and Engineering 121, no. 2 (May 1, 1999): 290–94. http://dx.doi.org/10.1115/1.2831218.
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Most manufacturing processes involve several process variables which interact with one another to produce a resultant action on the part. A fault is said to occur when any of these process variables deviate beyond their specified limits. An alarm is triggered when this happens. Low cost and less sophisticated detection schemes based on threshold bounds on the original measurements (without feature extraction) often suffer from high false alarm and missed detection rates when the process measurements are not properly conditioned. They are unable to detect frequency or phase shifted fault signals whose amplitudes remain within specifications. They also provide little or no information about the multiplicity (number of faults in the same process cycle) or location (the portion of the cycle where the fault was detected) of the fault condition. A method of overcoming these limitations is proposed in this paper. The Haar transform is used to generate sets of detection signals from the original measurements of process monitoring signals. By partitioning these signals into disjoint segments, mutually exclusive sets of Haar coefficients can be used to locate faults at different phases of the process. The lack of a priori information on fault condition is overcomed by using the Neyman-Pearson criteria for the uniformly most powerful form (UMP) of the likelihood ratio test (LRT).
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Campani, Claudia, Alessandro Vitale, Gabriele Dragoni, Umberto Arena, Giacomo Laffi, Umberto Cillo, Edoardo G. Giannini, et al. "Time-Varying mHAP-III Is the Most Accurate Predictor of Survival in Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization." Liver Cancer 10, no. 2 (2021): 126–36. http://dx.doi.org/10.1159/000513404.
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<b><i>Introduction:</i></b> The prognosis of patients undergoing transarterial chemoembolization (TACE) is extremely variable, and a confounding factor is that TACE is often repeated several times. We retrospectively evaluated the accuracy of different prognostic scores and staging systems in estimating overall survival (OS) in patients with hepatocellular carcinoma (HCC). <b><i>Methods:</i></b> An analysis considering prognostic models as time-varying variables was performed, calculating OS from the time of TACE to the time of the subsequent treatment. Total follow-up time for each patient was therefore split into several observation times accounting for each TACE procedure. Values of the likelihood ratio test (LRT) and Akaike information criterion (AIC) were used to compare different systems. Univariable and multivariable analyses were conducted to identify additional factors predictive of OS. We analyzed 1,610 TACE performed in 1,058 patients recorded in the Italian Liver Cancer database from 2008 through 2016. <b><i>Results:</i></b> The median OS of the enrolled patients was 41 months. According to LRT χ<sup>2</sup> and AIC values based on the time-varying analysis, mHAP-III achieved the best values (41.72 and 4,625.49, respectively, <i>p</i> < 0.0001), indicating the highest predictive performance compared with all other scores (HAP, mHAP-II, ALBI, and pALBI) and staging systems (MELD, ITALICA, CLIP, MESH, MESIAH, JIS, HKLC, and BCLC). In the multivariable Cox proportional hazards model, mHAP-III maintained an independent effect on OS (hazard ratio 1.31, 95% CI: 1.10–1.55, <i>p</i> < 0.0001). Time-varying age, alcoholic etiology, radiologic response to TACE, and performing ablation or surgery after TACE were additional significant variables resulting from the multivariable model. <b><i>Conclusion:</i></b> An innovative time-varying analysis revealed that mHAP-III was the most accurate model in predicting OS in patients with HCC undergoing TACE. Other clinical pre- and post-TACE variables were also found to be relevant for this prediction.
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Nunes, Natalie, Gareth Ambler, Wee-Liak Hoo, Joel Naftalin, Xulin Foo, Martin Widschwendter, and Davor Jurkovic. "A Prospective Validation of the IOTA Logistic Regression Models (LR1 and LR2) in Comparison to Subjective Pattern Recognition for the Diagnosis of Ovarian Cancer." International Journal of Gynecologic Cancer 23, no. 9 (November 2013): 1583–89. http://dx.doi.org/10.1097/igc.0b013e3182a6171a.
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ObjectivesThis study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.Methods and MaterialsThis was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.ResultsThe AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92–0.97] and 0.93 (95% CI, 0.90–0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84–24.6) and a LR−ve of 0.049 (95% CI, 0.022–0.107). The corresponding LR+ve and LR−ve for LR1 were 3.33 (95% CI, 2.85–3.55) and 0.03 (95% CI, 0.01–0.10), and for LR2 were 3.58 (95% CI, 2.77–4.63) and 0.052 (95% CI, 0.022–0.123). The accuracy of PR was 0.942 (95% CI, 0.908–0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781–0.870) for LR1 and 0.836 (95% CI, 0.788–0.872) for LR2 (P < 0.001).ConclusionsThe AUC of the IOTA LR1 and LR2 were similar in nonexpert’s hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.
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Cai, Xiu-Rong, Zhan-Hong Chen, Qu Lin, Min Dong, Xiao-kun Ma, Jie Chen, Jing-yun Wen, and Xiang-yuan Wu. "Modified CLIP score with albumin-bilirubin grade in relation to prognostic value in HBV-related hepatocellular carcinoma patients treated with transcatheter arterial chemoembolization therapy." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 480. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.480.
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480 Background: Child-Pugh grade is widely used to assess hepatic function reserve, but it is relatively subjective for assessment of hepatic encephalopathy. A newly developed scoring system combining albumin and bilirubin, called ALBI grade, aims to assess liver function objectively. In prognosis prediction of hepatocellular carcinoma (HCC), The Cancer of the Liver Italian Program (CLIP) score is commonly used in clinical practice and includes Child-Pugh evaluation. We substituted ALBI grade for Child-Pugh grade to establish ALBI-CLIP system and conducted this study to validate the prognostic value of ALBI -CLIP in HBV-related HCC patients after TACE therapy. Methods: We retrospectively analyzed HBV-related HCC patients who received TACE therapy. Baseline data were collected and evaluated. Child-Pugh grade and ALBI grade were integrated into CLIP and ALBI-CLIP systems, respectively. Univariate and multivariate analyses were conducted to identify independent prognostic factors for overall survival. Comparisons of receiver operating characteristic (ROC) curves and likelihood ratio test (LRT) were used to compare the value of ALBI-CLIP, CLIP and TNM staging systems in predicting survival. Results: A total of 207 patients were included. 153 (73.9%) and 54 (26.1%) patients were classified as Child-Pugh grade A and B, respectively. But according to ALBI grade, 57 (27.5%), 136 (65.7%) and 14 (6.8%) of them were correspondingly divided into Grade 1, 2 and 3. Comparisons of ROC curves showed that ALBI-CLIP and CLIP had similar areas under the curve, both of which were larger than that of TNM system in predicting 3-month, 6-month, 1-year and 2-year survival. LRT indicated that both ALBI-CLIP and CLIP had larger χ2 values and smaller values of Akaike information criterion (AIC), compared with TNM system (χ2 = 29.771, 29.479, 9.105; AIC = 858.215, 858.069, 879.410 for ALBI-CLIP, CLIP and TNM, respectively). Conclusions: Our current study suggested that modified CLIP score with albumin-bilirubin grade retained prognostic value in HBV-related HCC treated with TACE therapy.
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Tordoff, M., S. Smith, E. Lopez Isac, A. Morris, S. Eyre, W. Thomson, and J. Bowes. "OP0014 HLA ASSOCIATIONS IN PATIENTS WITH JUVENILE IDIOPATHIC ARTHRITIS ASSOCIATED UVEITIS AND CLINICAL SUBTYPES." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 8.2–9. http://dx.doi.org/10.1136/annrheumdis-2021-eular.430.
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Background:Juvenile idiopathic arthritis (JIA) is a childhood onset rheumatic disease which is classified into seven different clinical subtypes based upon the ILAR classification criteria. The most common extra articular manifestation of JIA is its associated uveitis (JIAU); particularly chronic anterior uveitis (CAU). Uveitis is a serious complication with the potential to lead to visual impairment and blindness. The rheumatoid factor negative polyarthritis and oligoarthritis ILAR subtypes, often referred to as the “polygo” subgroup, are at a higher risk for developing JIAU, with up to 30% of polygos afflicted by CAU. The HLA region has long been reported as a genetic risk factor for JIA susceptibility, with evidence suggesting that different amino acids of HLA genes infer risk to different JIA subtypes.Objectives:Investigate the association of amino acids and genetic variants in the HLA region with susceptibility to JIAU and the ILAR clinical subtypes.Methods:Samples were genotyped using the Illumina Infinium CoreExome and Infinium Onmiexpress arrays. Samples were excluded based on <98% call rate, discrepancy between genetically inferred sex and database records, inferred relatedness (identify-by-descent) and ancestral outliers based on principal component analysis (PCA). SNPs were excluded based on <0.01 minor allele frequency (MAF), and call rate <98%. SNP2HLA was used to impute HLA amino acids, SNPs and alleles. Analysis was then executed on markers with an information score >0.9 and MAF >.01 using logistic regression or an omnibus test for multiallelic markers, including 3 PCs as covariates. Independent associations were identified using forward stepwise logistic regression including previously identified variants as covariates. Comparison of regression models was performed using a likelihood ratio test (LRT).Results:We analysed 7425 markers within the HLA region in 450 JIAU and 2024 JIA cases without uveitis. The most significant association was to amino acid positions 13 of HLA-DRB1 (p=2.9×10-30). Conditional analysis on DRB1 position 13 revealed an independent signal at DRB1 position 67 (p=2.4×10-6). Conditioning on all DRB1 alleles revealed an independent signal at HLA-DPB1 position 69 (p=5.3×10-7). As expected, ILAR subtype was found to be associated with JIAU (p=1.58×10-6). We used LRT to test if genetics provided further information above ILAR subtype alone and found that including residues at DRB1 position 13 significantly improved the fit of a model based on ILAR subtype alone (LRT p = 3.6×10-27). The reciprocal analysis, adding ILAR subtype to a model based on DRB1 position 13 alone, did not significantly improve the fit of a model (LRT p=0.83). Exploring associations in the polygo subgroup (n=1646) we found significant associations to the three previously described amino acids and JIAU (DRB1 position 13 p=3.4×10-20, DRB1 position 67 p=3.3×10-4, DPB1 position 69 p=2.2×10-6).Conclusion:This is largest analysis of HLA markers in JIAU patients to date and we identify two independent associations to amino acids in HLA-DRB1 and a further independent association to HLA-DPB1. This analysis demonstrates that including data on genetic risk factors adds further information to that captured by ILAR subtype alone. It also reveals that the previously validated associations at position 13 of HLA-DRB1 are also correlated with JIAU in the polygo subtype suggesting that genetic risk factors will help refine risk within clinical subtypes. Conditioning on DRB1 alleles reveals that the secondary independent DRB1 position 69 association is also strongly associated in the polygo subset of this cohort. Together these results highlight the potential future use of genetics risk factors for risk classification for uveitis in patients with JIADisclosure of Interests:None declared
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Naderi, Yousef. "Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models." Genetika 51, no. 1 (2019): 17–29. http://dx.doi.org/10.2298/gensr1901017n.
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The primary concern of this study is to investigate appropriate random regression model for estimate genetic parameters body weight at hatch (BW1), eight (BW8), twelve (BW12) and thirty two (BW32) weeks of ages by the restricted maximum likelihood method. The body weight records set included 39872 during 16 generations of hens kept at the Mazandaran Breeding Center of Iran. Random regression were modelled using generation-hatch as a fixed effect and additive genetic and permanent environmental effects as random effects Residual variances were modeled through a step function with 1 and 3 classes. The model was considered to be the most appropriate with the highest significant log likelihood ratio test (LRT) and the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). Heritability values increased from 0.21 for BW1, to 0.40 for BW32. Genetic correlations of body weight at different record keeping were often higher than permanent environmental correlations. Genetic correlations between pairs of body weight measures were moderate to high with a range from 0.25 to 0.97. The largest genetic correlation, as well as permanent environmental correlation, was observed between BW12and BW32. High and moderate broad sense heritability values for all studied traits shows that these traits are less influenced by residual effects which make them effectively transmitted to the progeny. Findings show that genetic improvement for body weight can be achieved by selection. The Heritability of body weight at thirty two weeks of ages and its relatively high genetic correlation with all other ages showed that it could be the most appropriate period for selection. Also, the genetic trend estimates for body weight traits showed that selection decisions made during the breeding program effectively improved the growth performance.
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Shokraee, Kamyar, Hossein Mahdavi, Parsa Panahi, Farnoosh Seirafianpour, Amir Mohammad Jahromizadeh, Rozhin Tofighi, Ali Amirkafi, et al. "Accuracy of chest computed tomography and reverse transcription polymerase chain reaction in diagnosis of 2019 novel coronavirus disease; a systematic review and meta-analysis." Immunopathologia Persa 7, no. 2 (March 11, 2021): e36-e36. http://dx.doi.org/10.34172/ipp.2021.36.
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Introduction: This study aims to measure the diagnostic accuracy of chest computed tomography (CT) and reverse transcription polymerase chain reaction assay (RT-PCR) in COVID-19 in a systematic review and meta-analysis. Methods: PubMed, Scopus, Embase, and Google Scholar, WHO, SSRN, and MedRxiv have been searched on March 26, 2020 for all the alternative names of the disease and virus. Risk of bias assessment was based on QUADAS-2. Data from English-language studies after January 12, 2019 were pooled to calculate necessary diagnostic values and underwent diagnostic test accuracy, random-effects, proportions, and subgroup meta-analysis. Results: Pooled from 27 included studies, the sensitivity of chest CT was calculated 96.6%, specificity 22.5%, diagnostic odds ratio (DOR) 8.2, positive likelihood ratio (PLR) 1.2 (95% CI: 1.1-1.4), and negative likelihood ratio (NLR) 0.15 (95% CI: 0.1-0.3). The sensitivity for initial RT-PCR was 79.7%, the specificity 100%, and NLR 0.18. Conclusion: Considering the results, in order to diagnose COVID-19 (coronavirus disease 2019), it is recommended to initially performing chest CT to rule out the uninfected people. In suspicious cases, we suggest RT-PCR to confirm the disease. Performing serial RT-PCR instead of the one-time test is highly recommended, to let the viral loads reach the diagnostic levels, especially in cases of high clinical suspicion.
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Dickinson, Jake, Marcel de Matas, Paul A. Dickinson, and Hitesh B. Mistry. "Exploring a model-based analysis of patient derived xenograft studies in oncology drug development." PeerJ 9 (January 27, 2021): e10681. http://dx.doi.org/10.7717/peerj.10681.
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Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.
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Abegaz, S., J. B. van Wyk, and J. J. Olivier. "Estimates of (co)variance function for growth to yearling in Horro sheep of Ethiopia using random regression model." Archives Animal Breeding 53, no. 6 (October 10, 2010): 689–700. http://dx.doi.org/10.5194/aab-53-689-2010.
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Abstract. Random regression analyses of weight data from birth to 396 days were done using 22 141 weight records of 1 951 Horro lambs. Six different models formed from three different orthogonal polynomial regressions (legendre scale)orders (quadratic, cubic, quartic) of fit for both additive genetic and animals’ permanent environmental effects, with assumption of either homogeneous or heterogeneous residual variance, were compared. Fixed effects of year and type of birth, sex and age of dam were fitted along with a fourth order polynomial. Both likelihood ratio test (LRT) and Akaike's Information Criterion (AIC) were used for model comparison. Model fit improved with increased order of polynomial and assumption of heterogeneity of residual variance. Components for additive genetic and permanent environmental (co)variance increased from 0.03 and 0.09 at birth to 23.8 and 37.6 at 396 days of age, respectively. The first three eigenvalues of the coefficient matrix of the additive genetic covariance accounted for about 98 % of the sum of all the eigenvalues. Heritability estimates have shown a declining and increasing trend at different parts of the trajectory, the lowest estimate being 0.14 for weight at birth while the highest being 0.36 for weight at about 390 days of age. Higher heritability estimates in previous uni- and bi-variate models and in the current study and also strong correlation with weight at early age makes weight at one year of age the most important trait to consider in improving productivity in Horro sheep.
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Bux, Lal, Dalu Li, Muhammad Faheem, Nour Ali, Muzafar Hussain Sirohi, Mehtab Ali, Ali Nawaz Kumbhar, et al. "Detection of QTLs for Outcrossing-Related Traits in CSSL Population Derived from Primitive Japonica Accession Ludao in the Genetic Background of O. sativa spp. Japonica Restorer C-bao Using RSTEP-LRT Method." Agronomy 10, no. 1 (December 23, 2019): 28. http://dx.doi.org/10.3390/agronomy10010028.
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The outcrossing traits in rice (Oryza sativa L.) affect the yield of hybrid seed production. Using a cytoplasmic male sterile (CMS) line with good outcrossing traits, such as short flag leaf length (FLL), narrow flag leaf width (FLW), wide flag leaf angle (FLA), and elongated panicle neck length (PNL), for hybrid rice seed production, it is possible to avoid the procedure of cutting flag leaves and make the supplementary pollination feasible by machine. In this study, a japonica restorer C-bao as the receptor parent and a primitive japonica accession Ludao as the donor parent were used to construct a chromosome segment substitution line (CSSL) population. The CSSL population was used to detect quantitative trait loci (QTLs) for the four outcrossing traits using a likelihood ratio test based on the stepwise regression (RSTEP-LRT) method. The CSSL population constructed consisted of 163 lines covering 90.7% of the donor genome. Among the seven QTLs detected in the CSSL population, four QTLs were detected in both years. qFLL-4 explained 6.70% of the two-year-averaged phenotypic variance, and the alleles from Ludao decreased FLL 5.1 cm. qFLA-1.1 and qFLA-1.2 explained 7.85% and 21.29% of the 2-year-averaged phenotypic variance respectively, and the alleles from Ludao increased FLA 17.38° and 31.50°. qPNL-8 explained 8.87% of the 2-year-averaged phenotypic variance, and the alleles from Ludao increased PNL 4.44 cm. These favorable alleles identified could be used to improve the outcrossing traits of parents for hybrid rice seed production in rice.
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Babar, Laila, Ali Hussainy Zaidi, Ashten N. Omstead, Ping Zheng, Ronan Joseph Kelly, and Blair Anderson Jobe. "Prognostic immune markers for recurrence and survival in locally advanced esophageal adenocarcinoma." Journal of Clinical Oncology 37, no. 4_suppl (February 1, 2019): 50. http://dx.doi.org/10.1200/jco.2019.37.4_suppl.50.
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50 Background: Treatment options and risk stratification for esophageal adenocarcinomas (EAC) primarily rely on clinical and pathological criteria, such as tumor stage and pathological response (PR) evaluation. However, with immunotherapy showing hints of activity in EAC there is an emerging need to develop new predictive biomarkers that will not only identify high-risk patients but also unlock novel therapeutic strategies. The aim of this study was to evaluate if LAG3, TIM3, IDO and CXCL9 gene expression along with pathological classifiers correlate with recurrence and/or death in EAC. Methods: Clinical and histopathology data on 49 resectable EAC patients was captured. Mean age at diagnosis was 64.2 years with a median follow up of 21.2 months. Laser capture microdissection followed by qRT-PCR was performed on pretreatment FFPE samples to quantify LAG3, TIM3, IDO and CXCL9 gene levels. Statistical tools established individual biomarker cutoffs and these were correlated with clinical outcomes. Results: For disease free survival, a best fit Cox model was generated consisting of PR (HR=9.54, 95% CI = 2.13 ~ 42.62, p = .003), LAG3 (HR = 2.86, 95% CI = 1.03 ~ 7.94, p = .044), and CXCL9 (HR = 0.40, 95% CI = 0.16 ~ 0.99, p = .049). Moreover, the classification performance of the 3 predictor Cox model was deemed superior to PR alone; P = 0.0001 (Likelihood Ratio Test (LRT)) indicated strong significance in predictability with the recurrence model. For overall survival, a best fit Cox model was generated consisting of TIM3 (HR = 4.43, 95% CI= 1.70 ~ 11.5, p = .002) PR (HR = 3.09, 95% CI= 1.00 ~ 9.56, p = .051) and IDO (HR = 0.31, 95% CI = 0.11 ~ 0.82, p = .019). Likewise, the classification performance of the 3 predictor Cox model was deemed superior to PR alone; P = 0.0004 (LRT) indicated strong significance in predictability with the survival model. Conclusions: LAG3, TIM3, IDO and CXCL9 expression in conjunction with PR to neoadjuvant therapy provide superior tools to predict recurrence and death in locally advanced EAC patients. Additionally, given the paucity of treatment options for esophageal cancer we must check for these markers early on in the disease course to better risk stratify patients and provide newer targets for novel treatments.
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Longo, Cristina, Gillian Bartlett, Tibor Schuster, Francine M. Ducharme, Brenda MacGibbon, and Tracie A. Barnett. "Influence of weight status in the response to Step-2 maintenance therapies in children with asthma." BMJ Open Respiratory Research 6, no. 1 (April 2019): e000401. http://dx.doi.org/10.1136/bmjresp-2019-000401.
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IntroductionOverweight children with asthma may display impaired response to inhaled corticosteroids (ICS), possibly due to non-eosinophilic inflammation or weight-related lung compression; these mechanisms may differentially affect response to ICS and leukotriene receptor antagonists (LTRAs). We assessed whether weight status modified the response to low-dose ICS and LTRA Step-2 monotherapy.MethodsA historical cohort study from clinical data linked to administrative databases was conducted among children aged 2–18 years with specialist-diagnosed asthma who were initiating or continuing a Step-2 monotherapy from 2000 to 2007 at the Montreal Children’s Hospital Asthma Centre. The outcome was time-to-management failure defined as any step-up in therapy, acute care visit, hospitalisation or oral corticosteroids for asthma, whichever occurred first. The independent and joint effects of weight status (body mass index [BMI] percentile) and time-varying treatment on time-to-management failure were estimated with marginal structural Cox models. The likelihood ratio test (LRT) and relative excess risk due to interaction (RERI) were computed to assess treatment effect modification by weight status on the multiplicative and additive scales.ResultsOf the 433 and 85 visits with a low-dose ICS and LTRA prescription, respectively, 388 management failures occurred over 14 529 visit-weeks of follow-up. Children using LTRA compared with low-dose ICS tended to have an overall higher risk of early management failure (HR 1.52; 95% CI 0.72 to 3.22). Irrespective of treatment, the hazard of management failure increased by 5% (HR 1.05; 95% CI 1.01 to 1.10) for every 10-unit increase in BMI percentile. An additional hazard reduction of 17% (HR 0.83; 95% CI 0.70 to 0.99) was observed for every 10-unit increase in BMI percentile among LTRA users, but not for ICS (HR 0.95; 95% CI 0.86 to 1.04). The LRT indicated a departure from exact multiplicativity (p<0.0001), and the RERIs for ICS and LTRA were −0.05 (95% CI −0.14 to 0.05) and −0.52 (95% CI −1.76 to 0.71).ConclusionsWeight status was associated with earlier time-to-management failure in children prescribed Step-2 therapy. This hypothesis-generating study suggests that LTRA response increases in children with higher BMI percentiles, although further research is warranted to confirm findings.