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Information‐adaptive clinical trials with selective recruitment and binary outcomes
Author(s) -
Barrett James E.
Publication year - 2017
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.7353
Subject(s) - covariate , statistical power , adaptive design , computer science , clinical trial , clinical study design , statistics , econometrics , medicine , machine learning , mathematics
Selective recruitment designs preferentially recruit individuals who are estimated to be statistically informative onto a clinical trial. Individuals who are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information‐adaptive design, recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values, and how information is defined is a critical element of information‐adaptive designs. In this paper, we define and evaluate four different methods for quantifying statistical information. Using both experimental data and numerical simulations, we show that selective recruitment designs can offer a substantial increase in statistical power compared with randomised designs. In trials without selective recruitment, we find that allocating individuals to treatment arms according to information‐adaptive protocols also leads to an increase in statistical power. Consequently, selective recruitment designs can potentially achieve successful trials using fewer recruits thereby offering economic and ethical advantages. Copyright © 2017 John Wiley & Sons, Ltd.