WHY Calculating the sample size is essential to reduce the cost of a study and to prove the hypothesis effectively. HOW Referring to pilot studies and previous research studies, we can choose a proper hypothesis and simplify the studies by using a website or Microsoft Excel sheet that contains formulas for calculating sample size in the beginning stage of the study. MORE There are numerous ...

Optimum sample size is an essential component of any research. The main purpose of the sample size calculation is to determine the number of samples needed to detect significant changes in clinical parameters, treatment effects or associations after data gathering. It is not uncommon for studies to be underpowered and thereby fail to detect the existing treatment effects due to inadequate sampl...

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Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this appro...

Noninferiority trials comparing new treatment with an active standard control are becoming increasingly common. This article discusses relevant issues regarding their need, design, analysis and interpretation: the appropriate choice of control group, types of noninferiority trial, ethical considerations, sample size determination and potential pitfalls to consider.

The term "design" encompasses all the structural aspects of a study, notably the definition of the study sample, size of sample, method of treatment allocation, type of statistical design (randomised, cross-over, sequential, etc), and choice of outcome measures. The importance of this stage cannot be overemphasised since no amount of clever analysis later will be able to compensate for major de...

This paper shows how the probability, for a random confounding factor to reverse the estimate of ordinary regression, decreases exponentially with the sample size.

A random sample size version of the central limit theorem is obtained for a general class of symmetric statistics based on uniform spacings. An important application to goodness of fit test for a Poisson process is discussed.

We prove the following conjecture of Narayana: there are no nontrivial dominance reene-ments of the Smirnov two-sample test if and only if the two sample sizes are relatively prime. We also count the number of natural signiicance 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 sampl...