@article {Pugh757,
author = {Pugh, Stephanie L. and Torres-Saavedra, Pedro A.},
title = {Fundamental Statistical Concepts in Clinical Trials and Diagnostic Testing},
volume = {62},
number = {6},
pages = {757--764},
year = {2021},
doi = {10.2967/jnumed.120.245654},
publisher = {Society of Nuclear Medicine},
abstract = {This article explores basic statistical concepts of clinical trial design and diagnostic testing, or how one starts with a question, formulates it into a hypothesis on which a clinical trial is then built, and integrates it with statistics and probability, such as determining the probability of rejecting the null hypothesis when it is actually true (type I error) and the probability of failing to reject the null hypothesis when it 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 type I error in the presence of multiple testing is needed to control the error{\textquoteright}s inflation. Within diagnostic testing, identifying false-positive and false-negative results is critical to understanding the performance of a test. These are used to determine the sensitivity and specificity of a test along with the test{\textquoteright}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 to provide a basic understanding of clinical trial design and analysis, with references to allow the reader to explore various concepts at a more detailed level if desired.},
issn = {0161-5505},
URL = {https://jnm.snmjournals.org/content/62/6/757},
eprint = {https://jnm.snmjournals.org/content/62/6/757.full.pdf},
journal = {Journal of Nuclear Medicine}
}