This paper is designed as a tool that a researcher could use in planning and conducting quality research. This is a review paper which gives a discussion of various aspects of designing consideration in medical research. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for survey type of studies, observation st...

We consider the sample size determination (SSD) problem, which is a basic yet extremely important aspect of experimental design. Specifically, we deal with the Bayesian approach to SSD, which gives researchers the possibility of taking into account pre-experimental information and uncertainty on unknown parameters. At the design stage, this fact offers the advantage of removing or mitigating ty...

The problem motivating the paper is the determination of sample size in clinical trials under normal likelihoods and at the substantive testing stage of a financial audit where normality is not an appropriate assumption. A combination of analytical and simulation-based techniques within the Bayesian framework is proposed. The framework accommodates two different prior distributions: one is the ...

Sample size estimation is a major component of the design of virtually every experiment in medicine. Prudent use of the available prior information is a crucial element of experimental planning. Most sample size formulae in current use employ this information only in the form of point estimates, even though it is usually more accurately expressed as a distribution over a range of values. In thi...

We have made corrections to the routines that were provided to implement Pounds and Cheng (2005) method to determine the sample size for a microarray experiment that uses the false discovery rate as the ultimate measure of statistical significance. Some routines in the original R and S-plus libraries did not properly account for differences between the definition of the noncentrality parameter ...

Abstract Background We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in. Methods illustrate the approach using diagnosis of ovarian cancer ( n = 5914, 33% event fraction) and obstructive coronary artery disease (CAD; 4888, 44% fraction). used logistic regression to develop a co...

Abstract We discuss an important issue that is not directly related to the main theses of van Doorn et al. ( Computational Brain and Behavior , 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how determine sample size in advance running a study planning Bayes factor analysis. adapt simulation-based method proposed by Wang Gelfand Statistical Science 193–208, ...