S size is a crucial component of research design. More often researchers want sample size to be justified on non statistical grounds. For example, a researcher may say that I could get only these many cases in the specified time period so as a statistician do something to make the study valid in spite of whatever sample size I have used. Similarly, financial constraint is sometimes cited an exc...

Two-stage trial designs provide the flexibility to stop early for efficacy or futility and are popular because they have a smaller sample size on average than a traditional trial has with the same type I and II error rates. This makes them financially attractive but also has the ethical benefit of reducing, in the long run, the number of patients who are given ineffective treatments. Designs th...

Randomised controlled trials (RCTs) are generally regarded as the gold standard for evaluating health care interventions. The level of uncertainty around a trial's estimate of effect is, however, frequently linked to how successful the trial has been in recruiting and retaining participants. As recruitment is often slower or more difficult than expected, with many trials failing to reach their ...

A method of computer simulation is applied to the investigation of problems connected with the genetic analysis of continuously variable behavioral characters in human populations. The efficiency with which various components of genetic and environmental variation can be detected is related to sample size. It is found that a convincing partition of the genetic variance into its additive and non...

Adaptive clinical trials are becoming very popular because of their flexibility in allowing mid-stream changes of sample size, endpoints, populations, etc. At the same time, they have been regarded with mistrust because they can produce bizarre results in very extreme settings. Understanding the advantages and disadvantages of these rapidly developing methods is a must. This paper reviews flexi...

This article extends previous research on the recovery of weak factor loadings in confirmatory factor analysis (CFA) by exploring the effects of adding the mean structure. This issue has not been examined in previous research. This study is based on the framework of Yung and Bentler (1999) and aims to examine the conditions that affect the recovery of weak factor loadings when the model include...

BACKGROUND Statistical methods that use the mid-p approach are useful tools to analyze categorical data, particularly for small and moderate sample sizes. Mid-p tests strike a balance between overly conservative exact methods and asymptotic methods that frequently violate the nominal level. Here, we examine a mid-p version of the McNemar exact conditional test for the analysis of paired binomia...

We prove the following conjecture of Narayana: there are no nontrivial dominance refinements of the Smirnov two-sample test if and only if the two sample sizes are relatively prime. We also count the number of natural significance levels of the Smirnov two-sample test in terms of the sample sizes and relate this to the Narayana conjecture. In particular, Smirnov tests with relatively prime samp...

This article examines effects of sample size and other design features on correspondence between factors obtained from analysis of sample data and those present in the population from which the samples were drawn. We extend earlier work on this question by examining these phenomena in the situation in which the common factor model does not hold exactly in the population. We present a theoretica...

Monte Carlo simulations are increasingly used to predict pharmacokinetic variability of antimicrobials in a population. We investigated the sample size necessary to provide robust pharmacokinetic predictions. To obtain reasonably robust predictions, a nonparametric model derived from a sample population size of >/=50 appears to be necessary as the input information.