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Inference and sample size calculation for clinical trials with incomplete observations of paired binary outcomes
Author(s) -
Zhang Song,
Cao Jing,
Ahn Chul
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7168
Subject(s) - estimator , sample size determination , inference , statistics , type i and type ii errors , binary data , binary number , sample (material) , computer science , statistical inference , mathematics , econometrics , intervention (counseling) , standard error , psychology , artificial intelligence , chemistry , arithmetic , chromatography , psychiatry
We investigate the estimation of intervention effect and sample size determination for experiments where subjects are supposed to contribute paired binary outcomes with some incomplete observations. We propose a hybrid estimator to appropriately account for the mixed nature of observed data: paired outcomes from those who contribute complete pairs of observations and unpaired outcomes from those who contribute either pre‐intervention or post‐intervention outcomes. We theoretically prove that if incomplete data are evenly distributed between the pre‐intervention and post‐intervention periods, the proposed estimator will always be more efficient than the traditional estimator. A numerical research shows that when the distribution of incomplete data is unbalanced, the proposed estimator will be superior when there is moderate‐to‐strong positive within‐subject correlation. We further derive a closed‐form sample size formula to help researchers determine how many subjects need to be enrolled in such studies. Simulation results suggest that the calculated sample size maintains the empirical power and type I error under various design configurations. We demonstrate the proposed method using a real application example. Copyright © 2016 John Wiley & Sons, Ltd.