Power calculations are derived for matched case-control studies in terms of the probability po of exposure among the control patients, the correlation coefficient phi for exposure between matched case and control patients, and the odds ratio psi for exposure in case and control patients. For given Type I and Type II error probabilities alpha and beta, the odds ratio that can be detected with a ...

Statistical power and thus the sample size required to achieve some desired level of power depend on the size of the effect of interest. However, effect sizes are seldom known exactly in psychological research. Instead, researchers often possess an estimate of an effect size as well as a measure of its uncertainty (e.g., a standard error or confidence interval). Previous proposals for planning ...

Our answer to the question posed in the title is negative. This intentionally provocative note discusses the issue of sample size in microarray studies from several angles. We suggest that the current view of microarrays as no more than a screening tool be changed and small sample studies no longer be considered appropriate.

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 ...

Two-stage designs are commonly used for Phase II trials. Optimal two-stage designs have the lowest expected sample size for a specific treatment effect, for example, the null value, but can perform poorly if the true treatment effect differs. Here we introduce a design for continuous treatment responses that minimizes the maximum expected sample size across all possible treatment effects. The p...

An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be sufficiently narrow. One AIPE method yields a sample size such that the expected width of the confiden...

Multiple comparisons are commonly made in epidemiologic and genetic research. How to appropriately adjust for multiple comparisons remains a controversial issue. This note demonstrates, however, that large increases in the number of comparisons has a limited effect on the sample size required to maintain an experimentwise alpha-level. In particular, the relative sample size required increases o...

Background The evaluation of treatments for rare diseases presents a number of challenges for trial practitioners, regulators and policy makers. Small sample sizes mean that ‘standard’ approaches to trial design and analysis may not be appropriate and alternatives such as Bayesian trial designs have been recommended [Lilford et al 1995]. However, little is known about the design and analysis ap...

Using Tukey-Kramer versus the ANOVA F-test as the omnibus test of the Hayter-Fisher procedure for comparing all pairs of normally distributed means, when sample sizes are unequal, is investigated. Simulation results suggest that using Tukey-Kramer leads to as much or more any-pairs power compared to using the F-test for certain patterns of mean differences, and equivalent per-pair and all-pairs...

In clinical trials, the statistical concepts of significance and power are used in the determination of sample size for trials. The trialist must provide an estimate of standard deviation and a hypothetical population difference to be detected. This must be modified to deal with the designs encountered in guideline research. These are cluster randomized trials, because the patients of a single ...