Articles de revues : « Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping »

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Jehan, Musarrat, et Efstratios Nikolaidis. « Bootstrapping and Separable Monte Carlo Simulation Methods Tailored for Efficient Assessment of Probability of Failure of Structural Systems ». SAE International Journal of Materials and Manufacturing 8, n^o 3 (14 avril 2015) : 609–15. http://dx.doi.org/10.4271/2015-01-0420.

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Ben Seghier, Mohamed el Amine, Mourad Bettayeb, José Correia, Abílio De Jesus et Rui Calçada. « Structural reliability of corroded pipeline using the so-called Separable Monte Carlo method ». Journal of Strain Analysis for Engineering Design 53, n^o 8 (22 juin 2018) : 730–37. http://dx.doi.org/10.1177/0309324718782632.

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The evaluation of the failure probability of corroded pipelines is an important calculation to quantify the risk assessment and integrity of pipelines. Traditional Monte Carlo simulation method has been widely used to solve this type of problems, where it generates a very large number of simulations and takes longer time in computing. In this study, enhanced computational method called Separable Monte Carlo is employed to evaluate the time-dependent reliability of pipeline segments containing active corrosion defects, where a practical example was used. The results show that the Separable Monte Carlo simulation method not only minimizes the computational cost strongly but also improves the calculation precision.

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Wayne Oldford, R. « Bootstrapping by monte carlo versus approximating the estimator and bootstrapping exactly : Cost and performance ». Communications in Statistics - Simulation and Computation 14, n^o 2 (janvier 1985) : 395–424. http://dx.doi.org/10.1080/03610918508812446.

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Wang, Yunfei, et Dejian Lai. « ASSESSING LOGISTIC REGRESSION BY BOOTSTRAPPING AND MONTE CARLO SIMULATION : MODELING LOW BIRTH WEIGHT ». JP Journal of Biostatistics 16, n^o 2 (10 juillet 2019) : 13–30. http://dx.doi.org/10.17654/bs016020013.

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Létourneau, Pascal, et Lars Stentoft. « Bootstrapping the Early Exercise Boundary in the Least-Squares Monte Carlo Method ». Journal of Risk and Financial Management 12, n^o 4 (15 décembre 2019) : 190. http://dx.doi.org/10.3390/jrfm12040190.

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This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross-sectional regressions to the fullest by averaging the individually obtained estimates at each early exercise step, starting from just before maturity, in the backwards induction algorithm. With this method, less errors are accumulated, and as a result of this, the price estimate is essentially unbiased even for long maturity options. Numerical results demonstrate the improvements from our method and show that these are robust to the choice of simulation setup, the characteristics of the option, and the dimensionality of the problem. Finally, because our method naturally disassociates the estimation of the optimal early exercise boundary from the pricing of the option, significant efficiency gains can be obtained by using less simulated paths and repetitions to estimate the optimal early exercise boundary than with the regular method.

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Lee, Hyo-Nam, et Jong-Yun Oh. « Monte Carlo Simulation on Reliability of a Self-Separable Ejector for Man-Portable Missiles ». International Journal of Aeronautical and Space Sciences 12, n^o 4 (30 décembre 2011) : 385–95. http://dx.doi.org/10.5139/ijass.2011.12.4.385.

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Tinungki, Georgina M. « Penentuan Distribusi Sample Terbatas Uji-J Davidson dan Mackinnon dengan Metode Bootstrap pada Model Regresi Tak Tersarang ». Jurnal Matematika Statistika dan Komputasi 15, n^o 2 (20 décembre 2018) : 1. http://dx.doi.org/10.20956/jmsk.v15i2.5708.

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Georgina Maria Tinungki* AbstractThere are some tests proposed for un-nested hypothesis between J-Davidson Test and MacKinnon Test. J’s Test is often bad result, but it always works very well when used bootstrap. Bootstrapping for J’s Test is expected to be able to show that by using bounded sample is better, because there is no fault in counting process. Moreover, bootstrapping J-Test will omit the possibility of inconsistence of the results test previously. Simulation result of Monte Carlo will compare the proposed bounded sample test with Cox and J’s Test previously. Keywords: un-nested hypothesis, J-Davidson Test, MacKinnon Test AbstrakTerdapat beberapa pengujian yang diusulkan untuk hipotesis tak tersarang antara lain Uji-J Davidson dan MacKinnon. Uji-J sering bekerja buruk, tetapi biasanya bekerja sangat baik ketika dibootstrapkan.. Bootstrapping Uji-J diharapkan mampuh menunjukkan sampel terbatas lebih baik karena tidak mempunyai kesalahan didalam proses perhitungan. Lebih dari itu, bootstrapping J-Tests akan mengeluarkan kemungkinan dari ketidak konsistenan hasil uji yang sebelumnya. Hasil Simulasi Monte Carlo membandingkan uji sampel terbatas yang diusulkan dengan test yang sebelumnya seperti Uji Cox dan J-Test. Kata Kunci: Hipotesis tak tersarang,, Uji-J Davidson, Uji MacKinnon

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Oppe, Mark, Daniela Ortín-Sulbarán, Carlos Vila Silván, Anabel Estévez-Carrillo et Juan M. Ramos-Goñi. « Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium : using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses ». European Journal of Health Economics 22, n^o 5 (20 avril 2021) : 711–21. http://dx.doi.org/10.1007/s10198-021-01285-1.

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Abstract Background Uncertainty in model-based cost-utility analyses is commonly assessed in a probabilistic sensitivity analysis. Model parameters are implemented as distributions and values are sampled from these distributions in a Monte Carlo simulation. Bootstrapping is an alternative method that requires fewer assumptions and incorporates correlations between model parameters. Methods A Markov model-based cost–utility analysis comparing oromucosal spray containing delta-9-tetrahidrocannabinol + cannabidiol (Sativex®, nabiximols) plus standard care versus standard spasticity care alone in the management of multiple sclerosis spasticity was performed over a 5-year time horizon from the Belgian healthcare payer perspective. The probabilistic sensitivity analysis was implemented using a bootstrap approach to ensure that the correlations present in the source clinical trial data were incorporated in the uncertainty estimates. Results Adding Sativex® spray to standard care was found to dominate standard spasticity care alone, with cost savings of €6,068 and a quality-adjusted life year gain of 0.145 per patient over the 5-year analysis. The probability of dominance increased from 29% in the first year to 94% in the fifth year, with the probability of QALY gains in excess of 99% for all years considered. Conclusions Adding Sativex® spray to spasticity care was found to dominate standard spasticity care alone in the Belgian healthcare setting. This study showed the use of bootstrapping techniques in a Markov model probabilistic sensitivity analysis instead of Monte Carlo simulations. Bootstrapping avoided the need to make distributional assumptions and allowed the incorporation of correlating structures present in the original clinical trial data in the uncertainty assessment.

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Olaniran, Oyebayo Ridwan, et Mohd Asrul Affendi Abdullah. « Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior ». Journal of Modern Applied Statistical Methods 18, n^o 2 (17 juillet 2020) : 2–13. http://dx.doi.org/10.22237/jmasm/1604188980.

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A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.

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Kimura, Daniel K. « Approaches to Age-Structured Separable Sequential Population Analysis ». Canadian Journal of Fisheries and Aquatic Sciences 47, n^o 12 (1 décembre 1990) : 2364–74. http://dx.doi.org/10.1139/f90-263.

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Modern statistical methods are being used more often to perform age-structured separable sequential population analysis (SSPA). This paper describes how some of these methods can be easily understood from a unified point of view. The approach is to begin with the now standard separable age-structured model, and modify some of the basic assumptions. The resulting models are examined using Monte Carlo simulation, with the mean square error of modeled biomass estimates used as the evaluation criterion. Simulation results indicate that nonlinear least squares and multinomial maximum likelihood are both capable of fitting lognormally and multinomially distributed catch-at-age data. It also appears that errors in modeling results introduced by ageing error may be minor, provided ageing error is of modest magnitude and is normally distributed. However, use of a somewhat incorrect functional form for the selectivities can cause greatly increased error in the modeling results, indicating that caution should be exercised when modeling selectivities. Results indicate that length-based SSPA is feasible. And finally, the models are used to provide insight into the old question of "how many fish should be aged?"

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Alfaro, M. E. « Bayes or Bootstrap ? A Simulation Study Comparing the Performance of Bayesian Markov Chain Monte Carlo Sampling and Bootstrapping in Assessing Phylogenetic Confidence ». Molecular Biology and Evolution 20, n^o 2 (1 février 2003) : 255–66. http://dx.doi.org/10.1093/molbev/msg028.

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Huang, Francis L. « Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters ». Educational and Psychological Measurement 78, n^o 2 (24 novembre 2016) : 297–318. http://dx.doi.org/10.1177/0013164416678980.

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Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

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Luyet, C., G. Schüpfer, M. Wipfli, R. Greif, M. Luginbühl et U. Eichenberger. « Different Learning Curves for Axillary Brachial Plexus Block : Ultrasound Guidance versus Nerve Stimulation ». Anesthesiology Research and Practice 2010 (2010) : 1–7. http://dx.doi.org/10.1155/2010/309462.

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Little is known about the learning of the skills needed to perform ultrasound- or nerve stimulator-guided peripheral nerve blocks. The aim of this study was to compare the learning curves of residents trained in ultrasound guidance versus residents trained in nerve stimulation for axillary brachial plexus block. Ten residents with no previous experience with using ultrasound received ultrasound training and another ten residents with no previous experience with using nerve stimulation received nerve stimulation training. The novices' learning curves were generated by retrospective data analysis out of our electronic anaesthesia database. Individual success rates were pooled, and the institutional learning curve was calculated using a bootstrapping technique in combination with a Monte Carlo simulation procedure. The skills required to perform successful ultrasound-guided axillary brachial plexus block can be learnt faster and lead to a higher final success rate compared to nerve stimulator-guided axillary brachial plexus block.

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Lo, Yoke-Lin, Johan G. C. van Hasselt, Siow-Chin Heng, Chin-Theam Lim, Toong-Chow Lee et Bruce G. Charles. « Population Pharmacokinetics of Vancomycin in Premature Malaysian Neonates : Identification of Predictors for Dosing Determination ». Antimicrobial Agents and Chemotherapy 54, n^o 6 (12 avril 2010) : 2626–32. http://dx.doi.org/10.1128/aac.01370-09.

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ABSTRACT The present study determined the pharmacokinetic profile of vancomycin in premature Malaysian infants. A one-compartment infusion model with first-order elimination was fitted to serum vancomycin concentration data (n = 835 points) obtained retrospectively from the drug monitoring records of 116 premature newborn infants. Vancomycin concentrations were estimated by a fluorescence polarization immunoassay. Population and individual estimates of clearance and distribution volume and the factors which affected the variability observed for the values of these parameters were obtained using a population pharmacokinetic modeling approach. The predictive performance of the population model was evaluated by visual inspections of diagnostic plots and nonparametric bootstrapping with replacement. Dosing guidelines targeting a value of ≥400 for the area under the concentration-time curve over 24 h in the steady state divided by the MIC (AUC24/MIC ratio) were explored using Monte Carlo simulation. Body size (weight), postmenstrual age, and small-for-gestational-age status are important factors explaining the between-subject variability of vancomycin pharmacokinetic parameter values for premature neonates. The typical population parameter estimates of clearance and distribution volume for a 1-kg premature appropriate-for-gestational-age neonate with a postmenstrual age of 30 weeks were 0.0426 liters/h and 0.523 liters, respectively. There was a 20% reduction in clearance for small-for-gestational-age infants compared to the level for the appropriate-for-gestational-age control. Dosage regimens based on a priori target response values were formulated. In conclusion, the pharmacokinetic parameter values for vancomycin in premature Malaysian neonates were estimated. Improved dosage regimens based on a priori target response values were formulated by incorporating body size, postmenstrual age, and small-for-gestational-age status, using Monte Carlo simulations with the model-estimated pharmacokinetic parameter values.

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Narsoo, Jason. « Performance Analysis of Portfolio Optimisation Strategies : Evidence from the Exchange Market ». International Journal of Economics and Finance 9, n^o 6 (15 mai 2017) : 124. http://dx.doi.org/10.5539/ijef.v9n6p124.

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Portfolio allocation is embedded in many decisional tasks for ensuring best returns under the constraint of minimising risk. In this paper, we implement several strategies in order to generate a holistic assessment of portfolio evaluation. The study analyses the performance of an extended framework of the classical tangency and targeted portfolio strategies. The extension is essentially the use of the skewed student-t distribution for the individual assets’ log-return. Our investigation is based on 15 currencies with US dollar as the base currency for the period spanning from 1999 to 2015. A comparative performance analysis between the portfolio optimization strategies is undertaken on the basis of various performance measures, namely the portfolio expected return, standard deviation, Beta coefficient, Sharpe Ratio, Jensen’s Alpha, Treynor ratio and Roy ratio. The portfolio VaR being perceived as one of the core metrics for risk management is also computed. It is actually proxied by 5 VaR estimates - the parametric Gaussian, the equally-weighted historical VaR, the bootstrapping historical VaR, the Monte-Carlo simulation VaR and the parametric GHD VaR. The results show that both tangency portfolios, with the Gaussian or the skewed student-t distribution perform best, particularly on the basis of highest Sharpe reward-to-variability ratio and lowest Value-at-Risk.

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Mogaka, John J. O., et Moses J. Chimbari. « Precision medicine implementation and research-practice partnerships : implications of measurement scale differential item functioning ». F1000Research 9 (28 juillet 2020) : 782. http://dx.doi.org/10.12688/f1000research.25010.1.

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Background: Omics-based biomarkers (OBMs) inform precision medicine (PM). As omics-based technologies gradually move into clinical settings, however, a co-occurrence of biomedical research and clinical practice is likely an important variable in the implementation of PM. Currently, little is known about the implications of such research-practice co-occurrence. Methods: This study used data collected from a pilot study designed to inform a full-scale PM implementation study through the validation of the measurement tool. It applied item response theory (IRT) methods to assess the tool’s reliability and measurement invariance across two study subgroups associated with research and practice settings. Results: The study sample consisted of 31 participants. Measurement invariance assessment was through differential item functioning (DIF) analysis with bootstrapping through Monte Carlo simulation. Overall, 13 out of 22 items that formed the PMI scale had DIF at significance level α=0.25. Item response functions (IRFs) revealed how each subgroup members responded to scale items and their attitudes towards factors that influence PM implementation. Conclusions: Attitudinal similarities and differences towards factors influencing PM implementation amongst those in biomedical research as compared with those in practice were established. Results indicated PM implementation knowledge that is unique and common to both groups. The study established the validity and reliability of the new PM implementation measurement tool for the two subgroups.

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Pietraszek, Jacek, Norbert Radek et Konrad Bartkowiak. « Advanced Statistical Refinement of Surface Layer’s Discretization in the Case of Electro-Spark Deposited Carbide-Ceramic Coatings Modified by a Laser Beam ». Solid State Phenomena 197 (février 2013) : 198–202. http://dx.doi.org/10.4028/www.scientific.net/ssp.197.198.

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Carbide coatings have numerous industrial applications due to their high abrasion, sliding and erosion resistance. The paper presents results of an advanced statistical analysis – involving auxiliary simulation methods like smooth bootstrapping and imputing of missing data – executed on surface layer profile of modified carbide-ceramic coatings. Source data were gathered in the previous research by Radek and Bartkowiak focused on microstructure analysis (SEM Joel JSM-5400), microhardness (Vickers method), roughness (FORM TALYSURF-120L) and adhesion (CSEM REVETEST)) tests. Anti-wear coating were first deposited on carbon steel C45 from WC-Co-Al2O3 electrodes in the process of the electro-spark alloying (ESA) by the EIL-8a apparatus. In the next step the coating were laser melted using impulse mode of Nd:YAG laser (BLS 720 model). Due to significant irregularity of collected data, the special methods of smoothing and imputing were involved based on Monte-Carlo methods. The collected data set was several times randomly divided into analytical and verification sub-sets and mentioned methods were applied. The results were used to calculate descriptive statistics like average values, variances, confidence intervals and smoothed histograms of probability distributions. The validity of the proposed approach was positively verified and it significantly improved quality of the results. The smoothing and imputing of data allow to avoid numerical artifacts that may arise during the classical statistical calculations on irregular data.

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Citores, Leire, Leire Ibaibarriaga et Ernesto Jardim. « Uncertainty estimation and model selection in stock assessment models with non-parametric effects on fishing mortality ». ICES Journal of Marine Science 75, n^o 2 (11 octobre 2017) : 585–95. http://dx.doi.org/10.1093/icesjms/fsx175.

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AbstractUncertainty coming from assessment models leads to risk in decision making and ignoring or misestimating it can result in an erroneous management action. Some parameters, such as selectivity or survey catchabilities, can present a wide range of shapes and the introduction of smooth functions, which up to now have not been widely used in assessment models, allows for more flexibility to capture underlying nonlinear structures. In this work a simulation study emulating a sardine population is carried out to compare three different methods for uncertainty estimation: multivariate normal distribution, bootstrap (without and with relative bias correction) and Markov chain Monte Carlo (MCMC). In order to study their performance depending on the model complexity, five different scenarios are defined depending on the shape of the smooth function of the fishing mortality. From 100 simulated datasets, performance is measured in terms of point estimation, coefficients of variation, bias, skewness, coverage probabilities, and correlation. In all approaches model fitting is carried out using the a4a framework. All three methods result in very similar performance. The main differences are found for observation variance parameters where the bootstrap and the multivariate normal approach result in underestimation of these parameters. In general, MCMC is considered to have better performance, being able to detect skewness, showing small relative bias and reaching expected coverage probabilities. It is also more efficient in terms of time consumption in comparison with bootstrapping.

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Sharples, Linda, Olympia Papachristofi, Saleema Rex et Sabine Landau. « Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis ». Clinical Trials 17, n^o 6 (20 août 2020) : 654–63. http://dx.doi.org/10.1177/1740774520947644.

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Background: Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions. Aims: This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it. Methods: Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon’s discretion. The primary outcome was return to normal heart rhythm at one year post surgery. Results: In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age. Conclusions: Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses.

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Norouzi, Mahdi, et Efstratios Nikolaidis. « Separable and Standard Monte Carlo Simulation of Linear Dynamic Systems Using Combined Approximations ». SAE International Journal of Commercial Vehicles 12, n^o 2 (25 janvier 2019). http://dx.doi.org/10.4271/02-12-02-0008.

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Geldof, Tine, Dusan Popovic, Nancy Van Damme, Isabelle Huys et Walter Van Dyck. « Nearest Neighbour Propensity Score Matching and Bootstrapping for Estimating Binary Patient Response in Oncology : A Monte Carlo Simulation ». Scientific Reports 10, n^o 1 (22 janvier 2020). http://dx.doi.org/10.1038/s41598-020-57799-w.

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Becker, Jurgen, Deon Meiring et Jan H. Van der Westhuizen. « Investigating the construct validity of an electronic in-basket exercise using bias-corrected bootstrapping and Monte Carlo re-sampling techniques ». SA Journal of Industrial Psychology 45 (26 septembre 2019). http://dx.doi.org/10.4102/sajip.v45i0.1582.

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Orientation: Technology-based simulation exercises are popular assessment measures for the selection and development of human resources.Research purpose: The primary goal of this study was to investigate the construct validity of an electronic in-basket exercise using computer-based simulation technology. The secondary goal of the study was to investigate how re-sampling techniques can be used to recover model parameters using small samples.Motivation for the study: Although computer-based simulations are becoming more popular in the applied context, relatively little is known about the construct validity of these measures.Research approach/design and method: A quantitative ex post facto correlational design was used in the current study with a convenience sample (N = 89). The internal structure of the simulation exercise was assessed using a confirmatory factor analytical approach. In addition, bias-corrected bootstrapping and Monte Carlo simulation strategies were used to assess the confidence intervals around model parameters.Main findings: Support was not found for the entire model, but only for one of the dimensions, namely, the Interaction dimension. Multicollinearity was found between most of the dimensions that were problematic for factor analyses.Practical/managerial implications: This study holds important implications for assessment practitioners who hope to develop unproctored simulation exercises.Contribution/value-add: This study aims to contribute to the existing debate regarding the validity and utility of assessment centres (ACs), as well as to the literature concerning the use of technology-driven ACs. In addition, the study aims to make a methodological contribution by demonstrating how re-sampling techniques can be used in small AC samples.

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Kleijnen, Jack P. C., et Wim C. M. van Beers. « Statistical Tests for Cross-Validation of Kriging Models ». INFORMS Journal on Computing, 31 août 2021. http://dx.doi.org/10.1287/ijoc.2021.1072.

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Kriging or Gaussian process models are popular metamodels (surrogate models or emulators) of simulation models; these metamodels give predictors for input combinations that are not simulated. To validate these metamodels for computationally expensive simulation models, the analysts often apply computationally efficient cross-validation. In this paper, we derive new statistical tests for so-called leave-one-out cross-validation. Graphically, we present these tests as scatterplots augmented with confidence intervals that use the estimated variances of the Kriging predictors. To estimate the true variances of these predictors, we might use bootstrapping. Like other statistical tests, our tests—with or without bootstrapping—have type I and type II error probabilities; to estimate these probabilities, we use Monte Carlo experiments. We also use such experiments to investigate statistical convergence. To illustrate the application of our tests, we use (i) an example with two inputs and (ii) the popular borehole example with eight inputs. Summary of Contribution: Simulation models are very popular in operations research (OR) and are also known as computer simulations or computer experiments. A popular topic is design and analysis of computer experiments. This paper focuses on Kriging methods and cross-validation methods applied to simulation models; these methods and models are often applied in OR. More specifically, the paper provides the following; (1) the basic variant of a new statistical test for leave-one–out cross-validation; (2) a bootstrap method for the estimation of the true variance of the Kriging predictor; and (3) Monte Carlo experiments for the evaluation of the consistency of the Kriging predictor, the convergence of the Studentized prediction error to the standard normal variable, and the convergence of the expected experimentwise type I error rate to the prespecified nominal value. The new statistical test is illustrated through examples, including the popular borehole model.

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Rezaian, Elham, Masoud Karimi, Ebrahim Nazari Far, Mahsa Yarelahi, Abdolrahim Asadollahi et Atefe Razaian. « Risk Assessment of Addiction and Tobacco Misuse in Community of the Rural Older Adult, Using Monte Carlo Simulation Sampling ». International Journal of High Risk Behaviors and Addiction In Press, In Press (8 décembre 2020). http://dx.doi.org/10.5812/ijhrba.106335.

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Background: Illegal drug abuse is an important challenge that is not adequately addressed with respect to gerontology in rural areas. Patients and Methods: In this cross-sectional study 641 people aged 60 years or older supported by rural health centers in Southern Iran were investigated (310 and 331 men and women, respectively). To take a sample size of 326, snowball sampling was used. Then, the sample size was developed and simulated to 1.65 million rural elderly using the Monte Carlo simulation and bootstrapping technique (upon the 2016 national census), calculated in SPSS V.25. The ASSIST-WHO, 2017 instrument was used to collect the data. Results: A significant difference was found between the mean sub-variables of marital status using the MANOVA. Widowhood and divorce had more impact on the tendency to addiction compared to the other factors on marital status. Nomads and farmers were more vulnerable to be addicted than those occupied in the public sector. It was also confirmed by the Eta2 coefficient that older men feeling loneliness have a higher tendency to commit risky behaviors. Conclusions: Being a nomad or framer and widowhood and divorce increased the tendency of male elderly towards illegal drugs. Further studies are required to develop guidelines for policy-makers concerning the rural aging community.

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Pesigan, Ivan Jacob Agaloos, et Shu Fai Cheung. « SEM-Based Methods to Form Confidence Intervals for Indirect Effect : Still Applicable Given Nonnormality, Under Certain Conditions ». Frontiers in Psychology 11 (18 décembre 2020). http://dx.doi.org/10.3389/fpsyg.2020.571928.

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A SEM-based approach using likelihood-based confidence interval (LBCI) has been proposed to form confidence intervals for unstandardized and standardized indirect effect in mediation models. However, when used with the maximum likelihood estimation, this approach requires that the variables are multivariate normally distributed. This can affect the LBCIs of unstandardized and standardized effect differently. In the present study, the robustness of this approach when the predictor is not normally distributed but the error terms are conditionally normal, which does not violate the distributional assumption of ordinary least squares (OLS) estimation, is compared to four other approaches: nonparametric bootstrapping, two variants of LBCI, LBCI assuming the predictor is fixed (LBCI-Fixed-X) and LBCI based on ADF estimation (LBCI-ADF), and Monte Carlo. A simulation study was conducted using a simple mediation model and a serial mediation model, manipulating the distribution of the predictor. The Monte Carlo method performed worst among the methods. LBCI and LBCI-Fixed-X had suboptimal performance when the distributions had high kurtosis and the population indirect effects were medium to large. In some conditions, the problem was severe even when the sample size was large. LBCI-ADF and nonparametric bootstrapping had coverage probabilities close to the nominal value in nearly all conditions, although the coverage probabilities were still suboptimal for the serial mediation model when the sample size was small with respect to the model. Implications of these findings in the context of this special case of nonnormal data were discussed.

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Drusano, G. L., Michael N. Neely, Sarah Kim, Walter M. Yamada, Stephan Schmidt, Brandon Duncanson, Jocelyn Nole, Nino Mtchedlidze, Charles A. Peloquin et Arnold Louie. « Building Optimal Three-Drug Combination Chemotherapy Regimens ». Antimicrobial Agents and Chemotherapy 64, n^o 11 (8 septembre 2020). http://dx.doi.org/10.1128/aac.01610-20.

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ABSTRACT Multidrug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with a synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify an optimal 3-drug therapy. We studied M. tuberculosis isolate H37Rv in log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen by employing a Monte Carlo simulation. Pretomanid plus moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (αs, αr-p, and αr-m) by bootstrapping showed the interaction was near synergistic. The addition of bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval regarding the decline in bacterial burden. The addition of bedaquiline/M2 metabolite shortened the time to eradication by 1 week and was significantly different. A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.

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Sanchez, Katherine, Isaac Kim, Brie Chun, Joanna Pucilowska, William L. Redmond, Walter J. Urba, Maritza Martel et al. « Multiplex immunofluorescence to measure dynamic changes in tumor-infiltrating lymphocytes and PD-L1 in early-stage breast cancer ». Breast Cancer Research 23, n^o 1 (7 janvier 2021). http://dx.doi.org/10.1186/s13058-020-01378-4.

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Abstract Background The H&E stromal tumor-infiltrating lymphocyte (sTIL) score and programmed death ligand 1 (PD-L1) SP142 immunohistochemistry assay are prognostic and predictive in early-stage breast cancer, but are operator-dependent and may have insufficient precision to characterize dynamic changes in sTILs/PD-L1 in the context of clinical research. We illustrate how multiplex immunofluorescence (mIF) combined with statistical modeling can be used to precisely estimate dynamic changes in sTIL score, PD-L1 expression, and other immune variables from a single paraffin-embedded slide, thus enabling comprehensive characterization of activity of novel immunotherapy agents. Methods Serial tissue was obtained from a recent clinical trial evaluating loco-regional cytokine delivery as a strategy to promote immune cell infiltration and activation in breast tumors. Pre-treatment biopsies and post-treatment tumor resections were analyzed by mIF (PerkinElmer Vectra) using an antibody panel that characterized tumor cells (cytokeratin-positive), immune cells (CD3, CD8, CD163, FoxP3), and PD-L1 expression. mIF estimates of sTIL score and PD-L1 expression were compared to the H&E/SP142 clinical assays. Hierarchical linear modeling was utilized to compare pre- and post-treatment immune cell expression, account for correlation of time-dependent measurement, variation across high-powered magnification views within each subject, and variation between subjects. Simulation methods (Monte Carlo, bootstrapping) were used to evaluate the impact of model and tissue sample size on statistical power. Results mIF estimates of sTIL and PD-L1 expression were strongly correlated with their respective clinical assays (p < .001). Hierarchical linear modeling resulted in more precise estimates of treatment-related increases in sTIL, PD-L1, and other metrics such as CD8+ tumor nest infiltration. Statistical precision was dependent on adequate tissue sampling, with at least 15 high-powered fields recommended per specimen. Compared to conventional t-testing of means, hierarchical linear modeling was associated with substantial reductions in enrollment size required (n = 25➔n = 13) to detect the observed increases in sTIL/PD-L1. Conclusion mIF is useful for quantifying treatment-related dynamic changes in sTILs/PD-L1 and is concordant with clinical assays, but with greater precision. Hierarchical linear modeling can mitigate the effects of intratumoral heterogeneity on immune cell count estimations, allowing for more efficient detection of treatment-related pharmocodynamic effects in the context of clinical trials. Trial registration NCT02950259.

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Articles de revues sur le sujet « Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping »

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