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You, Zhiying. "Power and sample size of cluster randomized trials." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/you.pdf.
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Gibbons, Christopher. "Determination of power and sample size for Levene's test." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447667.
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Cunningham, Tina. "Power and Sample Size for Three-Level Cluster Designs." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/148.
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Over the past few decades, Cluster Randomized Trials (CRT) have become a design of choice in many research areas. One of the most critical issues in planning a CRT is to ensure that the study design is sensitive enough to capture the intervention effect. The assessment of power and sample size in such studies is often faced with many challenges due to several methodological difficulties. While studies on power and sample size for cluster designs with one and two levels are abundant, the evaluation of required sample size for three-level designs has been generally overlooked. First, the nesting effect introduces more than one intracluster correlation into the model. Second, the variance structure of the estimated treatment difference is more complicated. Third, sample size results required for several levels are needed. In this work, we developed sample size and power formulas for the three-level data structures based on the generalized linear mixed model approach. We derived explicit and general power and sample size equations for detecting a hypothesized effect on continuous Gaussian outcomes and binary outcomes. To confirm the accuracy of the formulas, we conducted several simulation studies and compared the results. To establish a connection between the theoretical formulas and their applications, we developed a SAS user-interface macro that allowed the researchers to estimate sample size for a three-level design for different scenarios. These scenarios depend on which randomization level is assigned and whether or not there is an interaction effect.
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Chang, Yu-Wei. "Sample Size Determination for a Three-arm Biosimilar Trial." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/298932.
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Statistics<br>Ph.D.<br>The equivalence assessment usually consists of three tests and is often conducted through a three-arm clinical trial. The first two tests are to demonstrate the superiority of the test treatment and the reference treatment to placebo, and they are followed by the equivalence test between the test treatment and the reference treatment. The equivalence is commonly defined in terms of mean difference, mean ratio or ratio of mean differences, i.e. the ratio of the mean difference of the test and placebo to the mean difference of the reference and placebo. In this dissertation, the equivalence assessment for both continuous data and discrete data are discussed. For the continuous case, the test of the ratio of mean differences is applied. The advantage of this test is that it combines a superiority test of the test treatment over the placebo and an equivalence test through one hypothesis. For the discrete case, the two-step equivalence assessment approach is studied for both Poisson and negative binomial data. While a Poisson distribution implies that population mean and variance are the same, the advantage of applying a negative binomial model is that it accounts for overdispersion, which is a common phenomenon of count medical endpoints. The test statistics, power function, and required sample size examples for a three-arm equivalence trial are given for both continuous and discrete cases. In addition, discussions on power comparisons are complemented with numerical results.<br>Temple University--Theses
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Guan, Tianyuan. "Sample Size Calculations in Simple Linear Regression: A New Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667392849137.
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Tongur, Can. "Small sample performances of two tests for overidentifying restrictions." Thesis, Uppsala University, Department of Economics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6367.
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<p>Two new specification tests for overidentifying restrictions proposed by Hahn and Hausman (2002:b) are here tested and compared to the classical Sargan test. Power properties are found to be very similar in overall performance, while Sargan generally has better size than the new tests. Also, size is distorted for one of the new tests, thus a tendency to reject prevails. In addition, sometimes severe bias is found which affects the tests’ performances, something that differs from earlier studies.</p>
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Tong, Bo. "More accurate two sample comparisons for skewed populations." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/35783.
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Doctor of Philosophy<br>Department of Statistics<br>Haiyan Wang<br>Various tests have been created to compare the means of two populations in many scenarios and applications. The two-sample t-test, Wilcoxon Rank-Sum Test and bootstrap-t test are commonly used methods. However, methods for skewed two-sample data set are not well studied. In this dissertation, several existing two sample tests were evaluated and four new tests were proposed to improve the test accuracy under moderate sample size and high population skewness.
The proposed work starts with derivation of a first order Edgeworth expansion for the test statistic of the two sample t-test. Using this result, new two-sample tests based on Cornish Fisher expansion (TCF tests) were created for both cases of common variance and unequal variances. These tests can account for population skewness and give more accurate test results. We also developed three new tests based on three transformations (T[subscript i] test, i = 1; 2; 3) for the pooled case, which can be used to eliminate the skewness of the studentized statistic.
In this dissertation, some theoretical properties of the newly proposed tests are presented. In particular, we derived the order of type I error rate accuracy of the pooled two-sample t-test based on normal approximation (TN test), the TCF and T[subscript i] tests. We proved that these tests give the same theoretical type I error rate under skewness. In addition, we derived the power function of the TCF and TN tests as a function of the population parameters. We also provided the detailed conditions under which the theoretical power of the two-sample TCF test is higher than the two-sample TN test. Results from extensive simulation studies and real data analysis were also presented in this dissertation. The empirical results further confirm our theoretical results. Comparing with commonly used two-sample parametric and nonparametric tests, our new tests (TCF and Ti) provide the same empirical type I error rate but higher power.
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Sawrie, David Franklin. "Preemptive power analysis for the consulting statistician novel applications of internal pilot design and information based monitoring systems /." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2009r/sawrie.pdf.
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Bell, Melanie L., Amy L. Whitehead, and Steven A. Julious. "Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes." DOVE MEDICAL PRESS LTD, 2018. http://hdl.handle.net/10150/627081.
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Background: A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken. Methods: We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. Results: The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. Conclusion: Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.
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Senteney, Michael H. "A Monte Carlo Study to Determine Sample Size for Multiple Comparison Procedures in ANOVA." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160433478343909.
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Haardoerfer, Regine. "Power and Bias in Hierarchical Linear Growth Models: More Measurements for Fewer People." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/eps_diss/57.
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Hierarchical Linear Modeling (HLM) sample size recommendations are mostly made with traditional group-design research in mind, as HLM as been used almost exclusively in group-design studies. Single-case research can benefit from utilizing hierarchical linear growth modeling, but sample size recommendations for growth modeling with HLM are scarce and generally do not consider the sample size combinations typical in single-case research. The purpose of this Monte Carlo simulation study was to expand sample size research in hierarchical linear growth modeling to suit single-case designs by testing larger level-1 sample sizes (N1), ranging from 10 to 80, and smaller level-2 sample sizes (N2), from 5 to 35, under the presence of autocorrelation to investigate bias and power. Estimates for the fixed effects were good for all tested sample-size combinations, irrespective of the strengths of the predictor-outcome correlations or the level of autocorrelation. Such low sample sizes, however, especially in the presence of autocorrelation, produced neither good estimates of the variances nor adequate power rates. Power rates were at least adequate for conditions in which N2 = 20 and N1 = 80 or N2 = 25 and N1 = 50 when the squared autocorrelation was .25.Conditions with lower autocorrelation provided adequate or high power for conditions with N2 = 15 and N1 = 50. In addition, conditions with high autocorrelation produced less than perfect power rates to detect the level-1 variance.
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Ramasamy, Adaikalavan. "Increasing statistical power and generalizability in genomics microarray research." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81ccede7-a268-4c7a-9bf8-a2b68634846d.
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The high-throughput technologies developed in the last decade have revolutionized the speed of data accumulation in the life sciences. As a result we have very rich and complex data that holds great promise to solving many complex biological questions. One such technology that is very well established and widespread is DNA microarrays, which allows one to simultaneously measure the expression levels of tens of thousands of genes in a biological tissue. This thesis aims to contribute to the development of statistics that allow the end users to obtain robust and meaningful results from DNA microarrays for further investigation. The methodology, implementation and pragmatic issues of two important and related topics – sample size estimations for designing new studies and meta-analysis of existing studies – are presented here to achieve this aim. Real life case studies and guided steps are also given. Sample size estimation is important at the design stage to ensure a study has sufficient statistical power to address the stated objective given the financial constraints. The commonly used formula for estimating the number of biological samples, its short-comings and potential amelioration are discussed. The optimal number of biological samples and number of measurements per sample that minimizes the cost is also presented. Meta-analysis or the synthesis of information from existing studies is very attractive because it can increase the statistical power by making comprehensive and inexpensive use of available information. Furthermore, one can also easily test the generalizability of findings (i.e. the extent of results from a particular valid study can be applied to other circumstances). The key issues in conducting a meta-analysis for microarrays studies, a checklist and R codes are presented here. Finally, the poor availability of raw data in microarray studies is discussed here with recommendations for authors, journal editors and funding bodies. Good availability of data is important for meta-analysis in order to avoid biased results and for sample size estimation.
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Brown, Eric C. "Estimates of statistical power and accuracy for latent trajectory class enumeration in the growth mixture model." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000622.
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Awuor, Risper Akelo. "Effect of Unequal Sample Sizes on the Power of DIF Detection: An IRT-Based Monte Carlo Study with SIBTEST and Mantel-Haenszel Procedures." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28321.
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This simulation study focused on determining the effect of unequal sample sizes on statistical power of SIBTEST and Mantel-Haenszel procedures for detection of DIF of moderate and large magnitudes. Item parameters were estimated by, and generated with the 2PLM using WinGen2 (Han, 2006). MULTISIM was used to simulate ability estimates and to generate response data that were analyzed by SIBTEST. The SIBTEST procedure with regression correction was used to calculate the DIF statistics, namely the DIF effect size and the statistical significance of the bias. The older SIBTEST was used to calculate the DIF statistics for the M-H procedure. SAS provided the environment in which the ability parameters were simulated; response data generated and DIF analyses conducted. Test items were observed to determine if a priori manipulated items demonstrated DIF. The study results indicated that with unequal samples in any ratio, M-H had better Type I error rate control than SIBTEST. The results also indicated that not only the ratios, but also the sample size and the magnitude of DIF influenced the behavior of SIBTEST and M-H with regard to their error rate behavior. With small samples and moderate DIF magnitude, Type II errors were committed by both M-H and SIBTEST when the reference to focal group sample size ratio was 1:.10 due to low observed statistical power and inflated Type I error rates.<br>Ph. D.
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Matsouaka, Roland Albert. "Contributions to Imputation Methods Based on Ranks and to Treatment Selection Methods in Personalized Medicine." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10078.
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The chapters of this thesis focus two different issues that arise in clinical trials and propose novel methods to address them. The first issue arises in the analysis of data with non-ignorable missing observations. The second issue concerns the development of methods that provide physicians better tools to understand and treat diseases efficiently by using each patient's characteristics and personal biomedical profile. Inherent to most clinical trials is the issue of missing data, specially those that arise when patients drop out the study without further measurements. Proper handling of missing data is crucial in all statistical analyses because disregarding missing observations can lead to biased results. In the first two chapters of this thesis, we deal with the "worst-rank score" missing data imputation technique in pretest-posttest clinical trials. Subjects are randomly assigned to two treatments and the response is recorded at baseline prior to treatment (pretest response), and after a pre-specified follow-up period (posttest response). The treatment effect is then assessed on the change in response from baseline to the end of follow-up time. Subjects with missing response at the end of follow-up are assign values that are worse than any observed response (worst-rank score). Data analysis is then conducted using Wilcoxon-Mann-Whitney test. In the first chapter, we derive explicit closed-form formulas for power and sample size calculations using both tied and untied worst-rank score imputation, where the worst-rank scores are either a fixed value (tied score) or depend on the time of withdrawal (untied score). We use simulations to demonstrate the validity of these formulas. In addition, we examine and compare four different simplification approaches to estimate sample sizes. These approaches depend on whether data from the literature or a pilot study are available. In second chapter, we introduce the weighted Wilcoxon-Mann-Whitney test on un-tied worst-rank score (composite) outcome. First, we demonstrate that the weighted test is exactly the ordinary Wilcoxon-Mann-Whitney test when the weights are equal. Then, we derive optimal weights that maximize the power of the corresponding weighted Wilcoxon-Mann-Whitney test. We prove, using simulations, that the weighted test is more powerful than the ordinary test. Furthermore, we propose two different step-wise procedures to analyze data using the weighted test and assess their performances through simulation studies. Finally, we illustrate the new approach using data from a recent randomized clinical trial of normobaric oxygen therapy on patients with acute ischemic stroke. The third and last chapter of this thesis concerns the development of robust methods for treatment groups identification in personalized medicine. As we know, physicians often have to use a trial-and-error approach to find the most effective medication for their patients. Personalized medicine methods aim at tailoring strategies for disease prevention, detection or treatment by using each individual subject's personal characteristics and medical profile. This would result to (1) better diagnosis and earlier interventions, (2) maximum therapeutic benefits and reduced adverse events, (3) more effective therapy, and (4) more efficient drug development. Novel methods have been proposed to identify subgroup of patients who would benefit from a given treatment. In the last chapter of this thesis, we develop a robust method for treatment assignment for future patients based on the expected total outcome. In addition, we provide a method to assess the incremental value of new covariate(s) in improving treatment assignment. We evaluate the accuracy of our methods through simulation studies and illustrate them with two examples using data from two HIV/AIDS clinical trials.
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Hilario, Reginaldo Francisco. "Statistical modelling of data from performance of broiler chickens." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-18012019-165449/.
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Experiments with broiler chickens are common today, because due to the great market demand for chicken meat, the need to improve the factors related to the production of broiler chicken has arisen. Many studies have been done to improve handling techniques. In these studies statistical analysis methods and techniques are employed. In studies with comparisons between treatments, it is not uncommon to observe a lack of significant effect even when there is evidence to indicate the significance of the effects. In order to avoid such eventualities it is fundamental to carry out a good planning before conducting the experiment. In this context, a study of the power of the F test was made emphasizing the relationships between test power, sample size, mean difference to be detected and variance for chicken weights data. In the analysis of data from experiments with broilers with mixed sexes and that the experimental unit is the box, generally the models used do not take into account the variability between the sexes of the birds, this affects the precision of the inference on the population of interest . We propose a model for the total weight per box that takes into account the sex information of the broiler chickens.<br>Experimentos com frangos de corte são comuns atualmente, pois devido à grande demanda de mercado da carne de frango surgiu a necessidade de melhorar os fatores ligados à produção do frango de corte. Muitos estudos têm sido feitos para aprimorar as técnicas de manejo. Nesses estudos os métodos e técnicas estatísticas de análise são empregados. Em estudos com comparações entre tratamentos, não é incomum observar falta de efeito significativo mesmo quando existem evidências que apontam a significância dos efeitos. Para evitar tais eventualidades é fundamental realizar um bom planejamento antes da condução do experimento. Nesse contexto, foi feito um estudo do poder do teste F enfatizando as relações entre o poder do teste, tamanho da amostra, diferença média a ser detectada e variância para dados de pesos de frangos. Na análise de dados provenientes de experimentos com frangos de corte com ambos os sexos e que a unidade experimental é o boxe, geralmente os modelos utilizados não levam em conta a variabilidade entre os sexos das aves, isso afeta a precisão da inferência sobre a população de interesse. Foi proposto um modelo para o peso total por boxe que leva em conta a informação do sexo dos frangos.
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Kubrycht, Pavel. "Analýza síly testů hypotéz." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264617.
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This Thesis deals with the power of a statistical test and the associated problem of determining the appropriate sample size. It should be large enough to meet the requirements of the probabilities of errors of both the first and second kind. The aim of this Thesis is to demonstrate theoretical methods that result in derivation of formulas for minimum sample size determination. For this Thesis, three important probability distributions have been chosen: Normal, Bernoulli, and Exponential.
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Schäfer, Thomas, and Marcus A. Schwarz. "The Meaningfulness of Effect Sizes in Psychological Research: Differences Between Sub-Disciplines and the Impact of Potential Biases." Frontiers Media SA, 2019. https://monarch.qucosa.de/id/qucosa%3A33749.
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Effect sizes are the currency of psychological research. They quantify the results of a study to answer the research question and are used to calculate statistical power. The interpretation of effect sizes—when is an effect small, medium, or large?—has been guided by the recommendations Jacob Cohen gave in his pioneering writings starting in 1962: Either compare an effect with the effects found in past research or use certain conventional benchmarks. The present analysis shows that neither of these recommendations is currently applicable. From past publications without pre-registration, 900 effects were randomly drawn and compared with 93 effects from publications with pre-registration, revealing a large difference: Effects from the former (median r = 0.36) were much larger than effects from the latter (median r = 0.16). That is, certain biases, such as publication bias or questionable research practices, have caused a dramatic inflation in published effects, making it difficult to compare an actual effect with the real population effects (as these are unknown). In addition, there were very large differences in the mean effects between psychological sub-disciplines and between different study designs, making it impossible to apply any global benchmarks. Many more pre-registered studies are needed in the future to derive a reliable picture of real population effects.
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Charlier, Johan. "Monitoring gene level biodiversity - aspects and considerations in the context of conservation." Doctoral thesis, Stockholms universitet, Zoologiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-62796.
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The objectives of this thesis relate to questions needed to be addressed in the context of genetic monitoring for implementing the Convention on Biological Diversity for the gene level. Genetic monitoring is quantifying temporal changes in population genetic metrics. Specific goals of this thesis include i) synthesizing existing information relevant to genetic monitoring of Swedish species, ii) providing a genetic baseline for the Swedish moose, iii) evaluating the relative performance of nuclear versus organelle genetic markers for detecting population divergence, iv) actually monitoring the genetic composition, structure, level of variation, and effective population size (Ne) and assessing the relation between Ne and the actual number of individuals for an unexploited brown trout population. The concept of conservation genetic monitoring is defined and Swedish priority species for such monitoring are identified; they include highly exploited organisms such as moose, salmonid fishes, Norway spruce, Atlantic cod, and Atlantic herring. Results indicate that the Swedish moose might be more genetically divergent than previously anticipated and appears to be divided into at least three different subpopulations, representing a southern, a central, and a northern population. The relative efficiency of nuclear and organelle markers depends on the relationship between the degree of genetic differentiation at the two types of markers. In turn, this relates to how far the divergence process has progressed. For the monitored brown trout population no indication of systematic change of population structure or allele frequencies was observed over 30 years. Significant genetic drift was found, though, translating into an overall Ne-estimate of ~75. The actual number of adult fish (NC) was assessed as ~600, corresponding to an Ne/NC ratio of 0.13. In spite of the relatively small effective population size monitoring did not reveal loss of genetic variation.
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Jeng, Bennie Hau. "Power And Sample Size Considerations In A Pre-Maturely Terminated Randomized, Double-Mask, Placebo-Controlled, Dose-Response, Phase 2 Study Of The Safety And Efficacy Of Thymosin Beta 4 For The Treatment Of Persistent Corneal Epithelial Defects Resulting." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1323456256.
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Novakovic, Ana M. "Longitudinal Models for Quantifying Disease and Therapeutic Response in Multiple Sclerosis." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-316562.
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Treatment of patients with multiple sclerosis (MS) and development of new therapies have been challenging due to the disease complexity and slow progression, and the limited sensitivity of available clinical outcomes. Modeling and simulation has become an increasingly important component in drug development and in post-marketing optimization of use of medication. This thesis focuses on development of pharmacometric models for characterization and quantification of the relationships between drug exposure, biomarkers and clinical endpoints in relapse-remitting MS (RRMS) following cladribine treatment. A population pharmacokinetic model of cladribine and its main metabolite, 2-chloroadenine, was developed using plasma and urine data. The renal clearance of cladribine was close to half of total elimination, and was found to be a linear function of creatinine clearance (CRCL). Exposure-response models could quantify a clear effect of cladribine tablets on absolute lymphocyte count (ALC), burden of disease (BoD), expanded disability status scale (EDSS) and relapse rate (RR) endpoints. Moreover, they gave insight into disease progression of RRMS. This thesis further demonstrates how integrated modeling framework allows an understanding of the interplay between ALC and clinical efficacy endpoints. ALC was found to be a promising predictor of RR. Moreover, ALC and BoD were identified as predictors of EDSS time-course. This enables the understanding of the behavior of the key outcomes necessary for the successful development of long-awaited MS therapies, as well as how these outcomes correlate with each other. The item response theory (IRT) methodology, an alternative approach for analysing composite scores, enabled to quantify the information content of the individual EDSS components, which could help improve this scale. In addition, IRT also proved capable of increasing the detection power of potential drug effects in clinical trials, which may enhance drug development efficiency. The developed nonlinear mixed-effects models offer a platform for the quantitative understanding of the biomarker(s)/clinical endpoint relationship, disease progression and therapeutic response in RRMS by integrating a significant amount of knowledge and data.
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Salar, Kemal. "Sample size for correlation estimates." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27248.
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Jinks, R. C. "Sample size for multivariable prognostic models." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1354112/.
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Prognosis is one of the central principles of medical practice; useful prognostic models are vital if clinicians wish to predict patient outcomes with any success. However, prognostic studies are often performed retrospectively, which can result in poorly validated models that do not become valuable clinical tools. One obstacle to planning prospective studies is the lack of sample size calculations for developing or validating multivariable models. The often used 5 or 10 events per variable (EPV) rule (Peduzzi and Concato, 1995) can result in small sample sizes which may lead to overfitting and optimism. This thesis investigates the issue of sample size in prognostic modelling, and develops calculations and recommendations which may improve prognostic study design. In order to develop multivariable prediction models, their prognostic value must be measurable and comparable. This thesis focuses on time-to-event data analysed with the Cox proportional hazards model, for which there are many proposed measures of prognostic ability. A measure of discrimination, the D statistic (Royston and Sauerbrei, 2004), is chosen for use in this work, as it has an appealing interpretation and direct relationship with a measure of explained variation. Real datasets are used to investigate how estimates of D vary with number of events. Seeking a better alternative to EPV rules, two sample size calculations are developed and tested for use where a target value of D is estimated: one based on significance testing and one on confidence interval width. The calculations are illustrated using real datasets; in general the sample sizes required are quite large. Finally, the usability of the new calculations is considered. To use the sample size calculations, researchers must estimate a target value of D, but this can be difficult if no previous study is available. To aid this, published D values from prognostic studies are collated into a ‘library’, which could be used to obtain plausible values of D to use in the calculations. To expand the library further an empirical conversion is developed to transform values of the more widely-used C-index (Harrell et al., 1984) to D.
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Denne, Jonathan S. "Sequential procedures for sample size estimation." Thesis, University of Bath, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320460.
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Callan, Peggy Ann. "Developmental sentence scoring sample size comparison." PDXScholar, 1990. https://pdxscholar.library.pdx.edu/open_access_etds/4170.
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In 1971, Lee and Canter developed a systematic tool for assessing children's expressive language: Developmental Sentence Scoring (DSS). It provides normative data against which a child's delayed or disordered language development can be compared with the normal language of children the same age. A specific scoring system is used to analyze children's use of standard English grammatical rules from a tape-recorded sample of their spontaneous speech during conversation with a clinician.
The corpus of sentences for the DSS is obtained from a sample of 50 complete, different, consecutive, intelligible, non-echolalic sentences elicited from a child in conversation with an adult using stimulus materials in which the child is interested. There is limited research on the reliability of language samples smaller and larger than 50 utterances for DSS analysis.
The purpose of this study was to determine if there is a significant difference among the scores obtained from language samples of 25, 50, and 75 utterances when using the DSS procedure for children aged 6.0 to 6.6 years. Twelve children, selected on the basis of chronological age, normal receptive vocabulary skills, normal hearing, and a monolingual background, were chosen as subjects.
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Cámara, Hagen Luis Tomás. "A consensus based Bayesian sample size criterion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ64329.pdf.
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Ahn, Jeongyoun Marron James Stephen. "High dimension, low sample size data analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,375.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
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Nataša, Krklec Jerinkić. "Line search methods with variable sample size." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2014. http://dx.doi.org/10.2298/NS20140117KRKLEC.
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The problem under consideration is an unconstrained optimization problem with the objective function in the form of mathematical ex-pectation. The expectation is with respect to the random variable that represents the uncertainty. Therefore, the objective function is in fact deterministic. However, nding the analytical form of that objective function can be very dicult or even impossible. This is the reason why the sample average approximation is often used. In order to obtain reasonable good approximation of the objective function, we have to use relatively large sample size. We assume that the sample is generated at the beginning of the optimization process and therefore we can consider this sample average objective function as the deterministic one. However, applying some deterministic method on that sample average function from the start can be very costly. The number of evaluations of the function under expectation is a common way of measuring the cost of an algorithm. Therefore, methods that vary the sample size throughout the optimization process are developed. Most of them are trying to determine the optimal dynamics of increasing the sample size.The main goal of this thesis is to develop the clas of methods that can decrease the cost of an algorithm by decreasing the number of function evaluations. The idea is to decrease the sample size whenever it seems to be reasonable - roughly speaking, we do not want to impose a large precision, i.e. a large sample size when we are far away from the solution we search for. The detailed description of the new methods is presented in Chapter 4 together with the convergence analysis. It is shown that the approximate solution is of the same quality as the one obtained by dealing with the full sample from the start.Another important characteristic of the methods that are proposed here is the line search technique which is used for obtaining the sub-sequent iterates. The idea is to nd a suitable direction and to search along it until we obtain a sucient decrease in the function value. The sucient decrease is determined throughout the line search rule. In Chapter 4, that rule is supposed to be monotone, i.e. we are imposing strict decrease of the function value. In order to decrease the cost of the algorithm even more and to enlarge the set of suitable search directions, we use nonmonotone line search rules in Chapter 5. Within that chapter, these rules are modied to t the variable sample size framework. Moreover, the conditions for the global convergence and the R-linear rate are presented. In Chapter 6, numerical results are presented. The test problems are various - some of them are academic and some of them are real world problems. The academic problems are here to give us more insight into the behavior of the algorithms. On the other hand, data that comes from the real world problems are here to test the real applicability of the proposed algorithms. In the rst part of that chapter, the focus is on the variable sample size techniques. Different implementations of the proposed algorithm are compared to each other and to the other sample schemes as well. The second part is mostly devoted to the comparison of the various line search rules combined with dierent search directions in the variable sample size framework. The overall numerical results show that using the variable sample size can improve the performance of the algorithms signicantly, especially when the nonmonotone line search rules are used.The rst chapter of this thesis provides the background material for the subsequent chapters. In Chapter 2, basics of the nonlinear optimization are presented and the focus is on the line search, while Chapter 3 deals with the stochastic framework. These chapters are here to provide the review of the relevant known results, while the rest of the thesis represents the original contribution. <br>U okviru ove teze posmatra se problem optimizacije bez ograničenja pri čcemu je funkcija cilja u formi matematičkog očekivanja. Očekivanje se odnosi na slučajnu promenljivu koja predstavlja neizvesnost. Zbog toga je funkcija cilja, u stvari, deterministička veličina. Ipak, odredjivanje analitičkog oblika te funkcije cilja može biti vrlo komplikovano pa čak i nemoguće. Zbog toga se za aproksimaciju često koristi uzoračko očcekivanje. Da bi se postigla dobra aproksimacija, obično je neophodan obiman uzorak. Ako pretpostavimo da se uzorak realizuje pre početka procesa optimizacije, možemo posmatrati uzoračko očekivanje kao determinističku funkciju. Medjutim, primena nekog od determinističkih metoda direktno na tu funkciju moze biti veoma skupa jer evaluacija funkcije pod ocekivanjem često predstavlja veliki trošak i uobičajeno je da se ukupan trošak optimizacije meri po broju izračcunavanja funkcije pod očekivanjem. Zbog toga su razvijeni metodi sa promenljivom veličinom uzorka. Većcina njih je bazirana na odredjivanju optimalne dinamike uvećanja uzorka.Glavni cilj ove teze je razvoj algoritma koji, kroz smanjenje broja izračcunavanja funkcije, smanjuje ukupne trošskove optimizacije. Ideja je da se veličina uzorka smanji kad god je to moguće. Grubo rečeno, izbegava se koriscenje velike preciznosti (velikog uzorka) kada smo daleko od rešsenja. U čcetvrtom poglavlju ove teze opisana je nova klasa metoda i predstavljena je analiza konvergencije. Dokazano je da je aproksimacija rešenja koju dobijamo bar toliko dobra koliko i za metod koji radi sa celim uzorkom sve vreme.Još jedna bitna karakteristika metoda koji su ovde razmatrani je primena linijskog pretražzivanja u cilju odredjivanja naredne iteracije. Osnovna ideja je da se nadje odgovarajući pravac i da se duž njega vršsi pretraga za dužzinom koraka koja će dovoljno smanjiti vrednost funkcije. Dovoljno smanjenje je odredjeno pravilom linijskog pretraživanja. U čcetvrtom poglavlju to pravilo je monotono što znači da zahtevamo striktno smanjenje vrednosti funkcije. U cilju jos većeg smanjenja troškova optimizacije kao i proširenja skupa pogodnih pravaca, u petom poglavlju koristimo nemonotona pravila linijskog pretraživanja koja su modifikovana zbog promenljive velicine uzorka. Takodje, razmatrani su uslovi za globalnu konvergenciju i R-linearnu brzinu konvergencije.Numerički rezultati su predstavljeni u šestom poglavlju. Test problemi su razliciti - neki od njih su akademski, a neki su realni. Akademski problemi su tu da nam daju bolji uvid u ponašanje algoritama. Sa druge strane, podaci koji poticu od stvarnih problema služe kao pravi test za primenljivost pomenutih algoritama. U prvom delu tog poglavlja akcenat je na načinu ažuriranja veličine uzorka. Različite varijante metoda koji su ovde predloženi porede se medjusobno kao i sa drugim šemama za ažuriranje veličine uzorka. Drugi deo poglavlja pretežno je posvećen poredjenju različitih pravila linijskog pretraživanja sa različitim pravcima pretraživanja u okviru promenljive veličine uzorka. Uzimajuci sve postignute rezultate u obzir dolazi se do zaključcka da variranje veličine uzorka može značajno popraviti učinak algoritma, posebno ako se koriste nemonotone metode linijskog pretraživanja.U prvom poglavlju ove teze opisana je motivacija kao i osnovni pojmovi potrebni za praćenje preostalih poglavlja. U drugom poglavlju je iznet pregled osnova nelinearne optimizacije sa akcentom na metode linijskog pretraživanja, dok su u trećem poglavlju predstavljene osnove stohastičke optimizacije. Pomenuta poglavlja su tu radi pregleda dosadašnjih relevantnih rezultata dok je originalni doprinos ove teze predstavljen u poglavljima 4-6.
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Serra, Puertas Jorge. "Shrinkage corrections of sample linear estimators in the small sample size regime." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/404386.
Full textAbstract:
We are living in a data deluge era where the dimensionality of the data gathered by inexpensive sensors is growing at a fast pace, whereas the availability of independent samples of the observed data is limited. Thus, classical statistical inference methods relying on the assumption that the sample size is large, compared to the observation dimension, are suffering a severe performance degradation.
Within this context, this thesis focus on a popular problem in signal processing, the estimation of a parameter, observed through a linear model. This inference is commonly based on a linear filtering of the data. For instance, beamforming in array signal processing, where a spatial filter steers the beampattern of the antenna array towards a direction to obtain the signal of interest (SOI). In signal processing the design of the optimal filters relies on the optimization of performance measures such as the Mean Square Error (MSE) and the Signal to Interference plus Noise Ratio (SINR). When the first two moments of the SOI are known, the optimization of the MSE leads to the Linear Minimum Mean Square Error (LMMSE). When such statistical information is not available one may force a no distortion constraint towards the SOI in the optimization of the MSE, which is equivalent to maximize the SINR. This leads to the Minimum Variance Distortionless Response (MVDR) method.
The LMMSE and MVDR are optimal, though unrealizable in general, since they depend on the inverse of the data correlation, which is not known. The common approach to circumvent this problem is to substitute it for the inverse of the sample correlation matrix (SCM), leading to the sample LMMSE and sample MVDR. This approach is optimal when the number of available statistical samples tends to infinity for a fixed observation dimension. This large sample size scenario hardly holds in practice and the sample methods undergo large performance degradations in the small sample size regime, which may be due to short stationarity constraints or to a system with a high observation dimension.
The aim of this thesis is to propose corrections of sample estimators, such as the sample LMMSE and MVDR, to circumvent their performance degradation in the small sample size regime. To this end, two powerful tools are used, shrinkage estimation and random matrix theory (RMT). Shrinkage estimation introduces a structure on the filters that forces some corrections in small sample size situations. They improve sample based estimators by optimizing a bias variance tradeoff. As direct optimization of these shrinkage methods leads to unrealizable estimators, then a consistent estimate of these optimal shrinkage estimators is obtained, within the general asymptotics where both the observation dimension and the sample size tend to infinity, but at a fixed rate. That is, RMT is used to obtain consistent estimates within an asymptotic regime that deals naturally with the small sample size. This RMT approach does not require any assumptions about the distribution of the observations.
The proposed filters deal directly with the estimation of the SOI, which leads to performance gains compared to related work methods based on optimizing a metric related to the data covariance estimate or proposing rather ad-hoc regularizations of the SCM. Compared to related work methods which also treat directly the estimation of the SOI and which are based on a shrinkage of the SCM, the proposed filter structure is more general. It contemplates corrections of the inverse of the SCM and considers the related work methods as particular cases. This leads to performance gains which are notable when there is a mismatch in the signature vector of the SOI. This mismatch and the small sample size are the main sources of degradation of the sample LMMSE and MVDR. Thus, in the last part of this thesis, unlike the previous proposed filters and the related work, we propose a filter which treats directly both sources of degradation.<br>Estamos viviendo en una era en la que la dimensión de los datos, recogidos por sensores de bajo precio, está creciendo a un ritmo elevado, pero la disponibilidad de muestras estadísticamente independientes de los datos es limitada. Así, los métodos clásicos de inferencia estadística sufren una degradación importante, ya que asumen un tamaño muestral grande comparado con la dimensión de los datos. En este contexto, esta tesis se centra en un problema popular en procesado de señal, la estimación lineal de un parámetro observado mediante un modelo lineal. Por ejemplo, la conformación de haz en procesado de agrupaciones de antenas, donde un filtro enfoca el haz hacia una dirección para obtener la señal asociada a una fuente de interés (SOI). El diseño de los filtros óptimos se basa en optimizar una medida de prestación como el error cuadrático medio (MSE) o la relación señal a ruido más interferente (SINR). Cuando hay información sobre los momentos de segundo orden de la SOI, la optimización del MSE lleva a obtener el estimador lineal de mínimo error cuadrático medio (LMMSE). Cuando esa información no está disponible, se puede forzar la restricción de no distorsión de la SOI en la optimización del MSE, que es equivalente a maximizar la SINR. Esto conduce al estimador de Capon (MVDR). El LMMSE y MVDR son óptimos, pero no son realizables, ya que dependen de la inversa de la matriz de correlación de los datos, que no es conocida. El procedimiento habitual para solventar este problema es sustituirla por la inversa de la correlación muestral (SCM), esto lleva al LMMSE y MVDR muestral. Este procedimiento es óptimo cuando el tamaño muestral tiende a infinito y la dimensión de los datos es fija. En la práctica este tamaño muestral elevado no suele producirse y los métodos LMMSE y MVDR muestrales sufren una degradación importante en este régimen de tamaño muestral pequeño. Éste se puede deber a periodos cortos de estacionariedad estadística o a sistemas cuya dimensión sea elevada. El objetivo de esta tesis es proponer correcciones de los estimadores LMMSE y MVDR muestrales que permitan combatir su degradación en el régimen de tamaño muestral pequeño. Para ello se usan dos herramientas potentes, la estimación shrinkage y la teoría de matrices aleatorias (RMT). La estimación shrinkage introduce una estructura de los estimadores que mejora los estimadores muestrales mediante la optimización del compromiso entre media y varianza del estimador. La optimización directa de los métodos shrinkage lleva a métodos no realizables. Por eso luego se propone obtener una estimación consistente de ellos en el régimen asintótico en el que tanto la dimensión de los datos como el tamaño muestral tienden a infinito, pero manteniendo un ratio constante. Es decir RMT se usa para obtener estimaciones consistentes en un régimen asintótico que trata naturalmente las situaciones de tamaño muestral pequeño. Esta metodología basada en RMT no requiere suposiciones sobre el tipo de distribución de los datos. Los filtros propuestos tratan directamente la estimación de la SOI, esto lleva a ganancias de prestaciones en comparación a otros métodos basados en optimizar una métrica relacionada con la estimación de la covarianza de los datos o regularizaciones ad hoc de la SCM. La estructura de filtro propuesta es más general que otros métodos que también tratan directamente la estimación de la SOI y que se basan en un shrinkage de la SCM. Contemplamos correcciones de la inversa de la SCM y los métodos del estado del arte son casos particulares. Esto lleva a ganancias de prestaciones que son notables cuando hay una incertidumbre en el vector de firma asociado a la SOI. Esa incertidumbre y el tamaño muestral pequeño son las fuentes de degradación de los LMMSE y MVDR muestrales. Así, en la última parte de la tesis, a diferencia de métodos propuestos previamente en la tesis y en la literatura, se propone un filtro que trata de forma directa ambas fuentes de degradación.
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Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.
Full textAbstract:
Statistics<br>Ph.D.<br>This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed specifically for survival analysis. In both problems, the exponential survival model is assumed. The approach we propose for sample size determination is Bayesian decision theoretical, using explicitly a loss function and a prior distribution. The loss function used is the intrinsic discrepancy loss function introduced by Bernardo and Rueda (2002), and further expounded upon in Bernardo (2011). We use a conjugate prior, and investigate the sensitivity of the calculated sample sizes to specification of the hyper-parameters. For the second problem of blinded sample size re-estimation, we use prior predictive distributions to facilitate calculation of the interim test statistic in a blinded manner while controlling the Type I error. The determination of the test statistic in a blinded manner continues to be nettling problem for researchers. The first problem is typical of traditional experimental designs, while the second problem extends into the realm of adaptive designs. To the best of our knowledge, the approaches we suggest for both problems have never been done hitherto, and extend the current research on both topics. The advantages of our approach, as far as we see it, are unity and coherence of statistical procedures, systematic and methodical incorporation of prior knowledge, and ease of calculation and interpretation.<br>Temple University--Theses
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Vong, Camille. "Model-Based Optimization of Clinical Trial Designs." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-233445.
Full textAbstract:
General attrition rates in drug development pipeline have been recognized as a necessity to shift gears towards new methodologies that allow earlier and correct decisions, and the optimal use of all information accrued throughout the process. The quantitative science of pharmacometrics using pharmacokinetic-pharmacodynamic models was identified as one of the strategies core to this renaissance. Coupled with Optimal Design (OD), they constitute together an attractive toolkit to usher more rapidly and successfully new agents to marketing approval. The general aim of this thesis was to investigate how the use of novel pharmacometric methodologies can improve the design and analysis of clinical trials within drug development. The implementation of a Monte-Carlo Mapped power method permitted to rapidly generate multiple hypotheses and to adequately compute the corresponding sample size within 1% of the time usually necessary in more traditional model-based power assessment. Allowing statistical inference across all data available and the integration of mechanistic interpretation of the models, the performance of this new methodology in proof-of-concept and dose-finding trials highlighted the possibility to reduce drastically the number of healthy volunteers and patients exposed to experimental drugs. This thesis furthermore addressed the benefits of OD in planning trials with bio analytical limits and toxicity constraints, through the development of novel optimality criteria that foremost pinpoint information and safety aspects. The use of these methodologies showed better estimation properties and robustness for the ensuing data analysis and reduced the number of patients exposed to severe toxicity by 7-fold. Finally, predictive tools for maximum tolerated dose selection in Phase I oncology trials were explored for a combination therapy characterized by main dose-limiting hematological toxicity. In this example, Bayesian and model-based approaches provided the incentive to a paradigm change away from the traditional rule-based “3+3” design algorithm. Throughout this thesis several examples have shown the possibility of streamlining clinical trials with more model-based design and analysis supports. Ultimately, efficient use of the data can elevate the probability of a successful trial and increase paramount ethical conduct.
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Timberlake, Allison M. "Sample Size in Ordinal Logistic Hierarchical Linear Modeling." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/eps_diss/72.
Full textAbstract:
Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study.
Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM.
The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy.
Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category.
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Medeiros, José António Amaro Correia. "Optimal sample size for assessing bacterioneuston structural diversity." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/10901.
Full textAbstract:
Mestrado em Biologia Aplicada - Microbiologia Clínica e Ambiental<br>The surface microlayer (SML) is located at the interface atmospherehydrosphere
and is theoretically defined as the top millimeter of the water
column. However, the SML is operationally defined according to the sampling
method used and the thickness varies with weather conditions and organic
matter content, among other factors. The SML is a very dynamic compartment
of the water column involved in the process of transport of materials between
the hydrosphere and the atmosphere. Bacterial communities inhabiting the
SML (bacterioneuston) are expected to be adapted to the particular SML
environment which is characterized by physical and chemical stress associated
to surface tension, high exposure to solar radiation and accumulation of
hydrophobic compounds, some of which pollutants. However, the small
volumes of SML water obtained with the different sampling methods reported in
the literature, make the sampling procedure laborious and time-consuming.
Sample size becomes even more critical when microcosm experiments are
designed. The objective of this work was to determine the smallest sample size
that could be used to assess bacterioneuston diversity by culture independent
methods without compromising representativeness and therefore ecological
significance. For that, two extraction methods were tested on samples of 0,5
mL, 5 mL and 10 mL of natural SML obtained at the estuarine system Ria de
Aveiro. After DNA extraction, community structure was assessed by DGGE
profiling of rRNA gene sequences. The CTAB-extraction procedure was
selected as the most efficient extraction method and was later used with larger
samples (1 mL, 20 mL and 50 mL). The DNA obtained was once more
analyzed by DGGE and the results showed that the estimated diversity of the
communities does not increase proportionally with increasing sample size and
that a good estimate of the structural diversity of bacterioneuston communities
can be obtained with very small samples.<br>A microcamada superficial marinha (SML) situa-se na interface atmosferahidrosfera
e teoricamente é definida como o milímetro mais superficial da coluna
de água. Operacionalmente, a espessura da SML depende do método de
amostragem utilizado e é também variável com outros fatores, nomeadamente,
as condições meteorológicas e teor de matéria orgânica, entre outros. A SML é
um compartimento muito dinâmico da coluna de água que está envolvida no
processo de transporte de materiais entre a hidrosfera e a atmosfera. As
comunidades bacterianas que habitam na SML são designadas de
bacterioneuston e existem indícios de que estão adaptadas ao ambiente
particular da SML, caracterizado por stresse físico e químico associado à tensão
superficial, alta exposição à radiação solar e acumulação de compostos
hidrofóbicos, alguns dos quais poluentes de elevada toxicidade. No entanto, o
reduzido volume de água da SML obtidos em cada colheita individual com os
diferentes dispositivos de amostragem reportados na literatura, fazem com que
o procedimento de amostragem seja laborioso e demorado. O tamanho da
amostra torna-se ainda mais crítico em experiências de microcosmos.
O objectivo deste trabalho foi avaliar se amostras de pequeno volume podem
ser usadas para avaliar a diversidade do bacterioneuston, através de métodos
de cultura independente, sem comprometer a representatividade, e o significado
ecológico dos resultados. Para isso, foram testados dois métodos de extracção
em amostras de 0,5 mL, 5 mL e 10 mL de SML obtida no sistema estuarino da
Ria de Aveiro. Após a extracção do DNA total, a estrutura da comunidade
bacteriana foi avaliada através do perfil de DGGE das sequências de genes que
codificam para a sub unidade 16S do rRNA. O procedimento de extracção com
brometo de cetil trimetil de amônia (CTAB) foi selecionado como sendo o
método de extração com melhor rendimento em termos de diversidade do DNA
e mais tarde foi aplicado a amostras de maior dimensão (1 mL, 20 mL e 50 mL).
O DNA obtido foi mais uma vez usado para análise dos perfis de DGGE de 16S
rDNA da comunidade e os resultados mostraram que a estimativa da
diversidade de microorganismos não aumentou proporcionalmente com o
aumento do tamanho da amostra e que com amostras de pequeno volume
podem ser obtidas boas estimativas da diversidade estrutural das comunidades
de bacterioneuston.
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Kang, Qing. "Nonparametric tests of median for a size-biases sample /." Search for this dissertation online, 2005. http://wwwlib.umi.com/cr/ksu/main.
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Suen, Wai-sing Alan. "Sample size planning for clinical trials with repeated measurements." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B31972172.
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Tse, Kwok Ho. "Sample size calculation : influence of confounding and interaction effects /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?MATH%202006%20TSE.
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Suen, Wai-sing Alan, and 孫偉盛. "Sample size planning for clinical trials with repeated measurements." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31972172.
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McGrath, Neill. "Effective sample size in order statistics of correlated data." [Boise, Idaho] : Boise State University, 2009. http://scholarworks.boisestate.edu/td/32/.
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Islam, A. F. M. Saiful. "Loss functions, utility functions and Bayesian sample size determination." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1259.
Full textAbstract:
This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesian sample size determination (SSD) using loss or utility function with a linear cost function. A number of researchers have studied the Bayesian SSD problem. One group has considered utility (loss) functions and cost functions in the SSD problem and others not. Among the former most of the SSD problems are based on a symmetrical squared error (SE) loss function. On the other hand, in a situation when underestimation is more serious than overestimation or vice-versa, then an asymmetric loss function should be used. For such a loss function how many observations do we need to take to estimate the parameter under study? We consider different types of asymmetric loss functions and a linear cost function for sample size determination. For the purposes of comparison, firstly we discuss the SSD for a symmetric squared error loss function. Then we consider the SSD under different types of asymmetric loss functions found in the literature. We also introduce a new bounded asymmetric loss function and obtain SSD under this loss function. In addition, to estimate a parameter following a particular model, we present some theoretical results for the optimum SSD problem under a particular choice of loss function. We also develop computer programs to obtain the optimum SSD where the analytic results are not possible. In the two parameter exponential family it is difficult to estimate the parameters when both are unknown. The aim of the second part is to obtain an optimum decision for the two parameter exponential family under the two parameter conjugate utility function. In this case we discuss Lindley’s (1976) optimum decision for one 6 parameter exponential family under the conjugate utility function for the one parameter exponential family and then extend the results to the two parameter exponential family. We propose a two parameter conjugate utility function and then lay out the approximation procedure to make decisions on the two parameters. We also offer a few examples, normal distribution, trinomial distribution and inverse Gaussian distribution and provide the optimum decisions on both parameters of these distributions under the two parameter conjugate utility function.
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Norfleet, David Matthew. "Sample size effects related to nickel, titanium and nickel-titanium at the micron size scale." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187038020.
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Norfleet, David M. "Sample size effects related to nickel, titanium and nickel-titanium at the micron size scale." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1187038020.
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M'lan, Cyr Emile. "Bayesian sample size calculations for cohort and case-control studies." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82923.
Full textAbstract:
Sample size determination is one of the most important statistical issues in the early stages of any investigation that anticipates statistical analyses.<br>In this thesis, we examine Bayesian sample size determination methodology for interval estimation. Four major epidemiological study designs, cohort, case-control, cross-sectional and matched pair are the focus. We study three Bayesian sample size criteria: the average length criterion (ALC), the average coverage criterion ( ACC) and the worst outcome criterion (WOC ) as well as various extensions of these criteria. In addition, a simple cost function is included as part of our sample size calculations for cohort and case-controls studies. We also examine the important design issue of the choice of the optimal ratio of controls per case in case-control settings or non-exposed to exposed in cohort settings.<br>The main difficulties with Bayesian sample size calculation problems are often at the computational level. Thus, this thesis is concerned, to a considerable extent, with presenting sample size methods that are computationally efficient.
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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.
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Lee, Myung Hee Marron James Stephen. "Continuum direction vectors in high dimensional low sample size data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1132.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.<br>Title from electronic title page (viewed Mar. 27, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research Statistics." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
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Che, Huiwen. "Cutoff sample size estimation for survival data: a simulation study." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-234982.
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This thesis demonstrates the possible cutoff sample size point that balances goodness of es-timation and study expenditure by a practical cancer case. As it is crucial to determine the sample size in designing an experiment, researchers attempt to find the suitable sample size that achieves desired power and budget efficiency at the same time. The thesis shows how simulation can be used for sample size and precision calculations with survival data. The pre-sentation concentrates on the simulation involved in carrying out the estimates and precision calculations. The Kaplan-Meier estimator and the Cox regression coefficient are chosen as point estimators, and the precision measurements focus on the mean square error and the stan-dard error.
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Gilkey, Justin Michael. "The Effects of Sample Size on Measures of Subjective Correlation." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1211901739.
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Meganathan, Karthikeyan. "Sample Size Determination in Simple Logistic Regression: Formula versus Simulation." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663458916666.
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Song, Juhee. "Bootstrapping in a high dimensional but very low sample size problem." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3853.
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High Dimension, Low Sample Size (HDLSS) problems have received much attention
recently in many areas of science. Analysis of microarray experiments is one
such area. Numerous studies are on-going to investigate the behavior of genes by
measuring the abundance of mRNA (messenger RiboNucleic Acid), gene expression.
HDLSS data investigated in this dissertation consist of a large number of data sets
each of which has only a few observations.
We assume a statistical model in which measurements from the same subject
have the same expected value and variance. All subjects have the same distribution
up to location and scale. Information from all subjects is shared in estimating this
common distribution.
Our interest is in testing the hypothesis that the mean of measurements from a
given subject is 0. Commonly used tests of this hypothesis, the t-test, sign test and
traditional bootstrapping, do not necessarily provide reliable results since there are
only a few observations for each data set.
We motivate a mixture model having C clusters and 3C parameters to overcome
the small sample size problem. Standardized data are pooled after assigning each
data set to one of the mixture components. To get reasonable initial parameter estimates
when density estimation methods are applied, we apply clustering methods
including agglomerative and K-means.
Bayes Information Criterion (BIC) and a new criterion, WMCV (Weighted Mean
of within Cluster Variance estimates), are used to choose an optimal number of clusters.
Density estimation methods including a maximum likelihood unimodal density
estimator and kernel density estimation are used to estimate the unknown density.
Once the density is estimated, a bootstrapping algorithm that selects samples from
the estimated density is used to approximate the distribution of test statistics. The
t-statistic and an empirical likelihood ratio statistic are used, since their distributions
are completely determined by the distribution common to all subject. A method to
control the false discovery rate is used to perform simultaneous tests on all small data
sets.
Simulated data sets and a set of cDNA (complimentary DeoxyriboNucleic Acid)
microarray experiment data are analyzed by the proposed methods.
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Serrano, Daniel Curran Patrick J. "Error of estimation and sample size in the linear mixed model." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1653.
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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2008.<br>Title from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Psychology." Discipline: Psychology; Department/School: Psychology.
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Venkatesan, Harini. "Introduction to power and sample size in multilevel models." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5039.
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In this report we give a brief introduction to the multilevel models, provide a brief summary of the need for using the multilevel model, discuss the assumptions underlying use of multilevel models, and present by means of example the necessary steps involved in model building. This introduction is followed by a discussion of power and sample size determination in multilevel designs. Some formulae are discussed to provide insight into the design aspects that are most influential in terms of power and calculation of standard errors. Finally we conclude by discussing and reviewing the simulation study performed by Maas and Hox (2005) about the influence of different sample sizes at individual as well as group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors.<br>text