Sample size analysis for two-sample linear rank tests

Comparing two Formulas of Sample Size Determination for Prevalence Studies

Background and purpose: Sample size and its determination is one of the most important problems in health researches. Calculating sample size for prevalence studies is one of the common questions of sample size topics. Minimum sample size with least complexity is desirable in order to achieve the basic goal of these studies. This study aims to compare two formulas of sample size calculation for...

Symmetrized Approximate Score Rank Tests for the Two-sample Case

Sample size determination based on rank tests in clinical trials.

The problem of sample size determination based on three commonly used non-parametric rank based tests, namely, one-sample Wilcoxon's rank sum test, two-sample's Wilcoxon's rank sum test, and the rank-based test for independence is studied. Explicit formulas for variabilities of the test statistics under the alternative hypotheses are derived. Consequently, close forms of power functions of thes...

Linear Rank Tests for Two-Sample Fuzzy Data: A P-Value Approach

The linear rank test for two-sample data, which is a popular non-parametric statistical test, is extended to the case when the available data are imprecise rather than crisp. To do this, using some elements of credibility theory, we suggest a ranking method among imprecise observations and apply it to extend the usual concept of p-value leading a degree to accept or reject the null hypothesis o...

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...