Score confidence intervals and sample sizes for stratified comparisons of binomial proportions. 2020

Yongqiang Tang
Tesaro, Department of Biometrics, 1000 Winter Street, Waltham, MA, USA.

In a series of articles, Gart and Nam construct the efficient score tests and confidence intervals with or without skewness correction for stratified comparisons of binomial proportions on the risk difference, relative risk, and odds ratio effect metrics. However, the stratified score methods and their properties are not well understood. We rederive the efficient score tests, which reveals their theoretical relationship with the contrast-based score tests, and provides a basis for adapting the method by using other weighting schemes. The inverse variance weight is optimal for a common treatment effect in large samples. We explore the behavior of the score approach in the presence of extreme outcomes when either no or all subjects in some strata are responders, and provide guidance on the choice of weights in the analysis of rare events. The score method is recommended for studies with a small number of moderate or large sized strata. A general framework is proposed to calculate the asymptotic power and sample size for the score test in superiority, noninferiority and equivalence clinical trials, or case-control studies. We also describe a nearly exact procedure that underestimates the exact power, but the degree of underestimation can be controlled to a negligible level. The proposed methods are illustrated by numerical examples.

UI MeSH Term Description Entries
D012107 Research Design A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly. Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012306 Risk The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome. Relative Risk,Relative Risks,Risk, Relative,Risks,Risks, Relative
D016001 Confidence Intervals A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable. Confidence Interval,Interval, Confidence,Intervals, Confidence
D016017 Odds Ratio The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases. Cross-Product Ratio,Risk Ratio,Relative Odds,Cross Product Ratio,Cross-Product Ratios,Odds Ratios,Odds, Relative,Ratio, Cross-Product,Ratio, Risk,Ratios, Cross-Product,Ratios, Risk,Risk Ratios
D018401 Sample Size The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95) Sample Sizes,Size, Sample,Sizes, Sample

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