Premium
Adaptive Design and Estimation in Randomized Clinical Trials with Correlated Observations
Author(s) -
Yin Guosheng,
Shen Yu
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00333.x
Subject(s) - sample size determination , inference , statistics , confidence interval , computer science , data mining , null hypothesis , block (permutation group theory) , statistical power , statistical hypothesis testing , mathematics , artificial intelligence , geometry
Summary Clinical trial designs involving correlated data often arise in biomedical research. The intracluster correlation needs to be taken into account to ensure the validity of sample size and power calculations. In contrast to the fixed‐sample designs, we propose a flexible trial design with adaptive monitoring and inference procedures. The total sample size is not predetermined, but adaptively reestimated using observed data via a systematic mechanism. The final inference is based on a weighted average of the block‐wise test statistics using generalized estimating equations, where the weight for each block depends on cumulated data from the ongoing trial. When there are no significant treatment effects, the devised stopping rule allows for early termination of the trial and acceptance of the null hypothesis. The proposed design updates information regarding both the effect size and within‐cluster correlation based on the cumulated data in order to achieve a desired power. Estimation of the parameter of interest and its confidence interval are proposed. We conduct simulation studies to examine the operating characteristics and illustrate the proposed method with an example.