Fundamental Statistical Concepts in Clinical Trials and Diagnostic Testing.

Pugh, Stephanie L; Torres-Saavedra, Pedro

This article explores basic statistical concepts of clinical trial design and diagnostic testing. How one starts with a question, formulates it into a hypothesis upon which a clinical trial is then built, is integrated with statistics and probability, such as determining the probability of rejecting the null hypothesis when its actually true (type I error) and the probability of failing to reject the null hypothesis when the null hypothesis is false (type II error). There are a variety of tests for different types of data and the appropriate test must be chosen for which the sample data meet the assumptions. Correcting of the type I error in the presence of multiple testing is needed to control the error's inflation. Within diagnostic testing, identifying false positive and false negative patients is critical to understanding the performance of a test. These are utilized to determine the sensitivity and specificity of a test along with the test's negative predictive value and positive predictive value. These quantities, specifically sensitivity and specificity, are used to determine the accuracy of a diagnostic test using receiver operating characteristic curves. These concepts are briefly introduced, with references to allow the reader to explore various concepts at a more detailed level if desired, to provide a basic understanding of clinical trial design and analysis.