Power and sample size estimation constitutes an important component of designing and planning modern scientific studies. It provides information for assessing the feasibility of a study to detect treatment effects and for estimating the resources needed to conduct the project. This tutorial discusses the basic concepts of power analysis and the major differences between hypothesis testing and p...

One of the most common requests that statisticians get from investigators are sample size calculations or sample size justifications. The sample size is the number of patients or other experimental units included in a study, and determining the sample size required to answer the research question is one of the first steps in designing a study. Although most statistical textbooks describe techni...

Addressing a sample size is a practical issue that has to be solved during planning and designing stage of the study. The aim of any clinical research is to detect the actual difference between two groups (power) and to provide an estimate of the difference with a reasonable accuracy (precision). Hence, researchers should do a priori estimate of sample size well ahead, before conducting the stu...

One of the most common requests that statisticians get from investigators are sample size calculations or sample size justifications. The sample size is the number of patients or other experimental units included in a study, and determining the sample size required to answer the research question is one of the first steps in designing a study. Although most statistical textbooks describe techni...

Genetic association analysis using thousands of single nucleotide polymorphism (SNP) markers has become a promising alternative to genome-wide linkage scan. Analysis based on linkage-disequilibrium (LD) is more efficient because meiotic information of past generations is utilized. However, in addition to the physical distance between the disease locus and a marker locus, numerous other factors ...

Health care utilization and outcome studies call for hierarchical approaches. The objectives were to predict major complications following percutaneous coronary interventions by health providers, and to compare Bayesian and non-Bayesian sample size calculation methods. The hierarchical data structure consisted of: (1) Strata: PGY4, PGY7, and physician assistant as providers with varied experien...

Recently, Wu et al. [4] have proposed the sequence kernel machine test (SKAT) to test association between genetic variants in a gene or region and a continuous or binary trait. SKAT, which uses the kernel machine regression framework, is very flexible and computationally efficient. From extensive simulation studies and real data application, it has been shown that SKAT is more powerful than the...

INTRODUCTION Traditionally, biomechanical gait data are analyzed using discrete variables and a univariate statistical approach. In contrast, the multivariate and complex pathoaetiology of a running-related musculoskeletal injury often demands a more robust analysis [1]. Support Vector Machines (SVMs) have recently been applied to analyze biomechanical data due to its ability to identify comple...

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fail...