Covariate‐adjusted response‐adaptive randomization for multi‐arm clinical trials using a modified forward looking Gittins index rule

Covariate‐adjusted response‐adaptive randomization for multi‐arm clinical trials using a... IntroductionThe Gittins index rule (Gittins and Jones, ) was developed as an optimal solution to the classic multi‐armed bandit problem. In the context of a clinical trial to test the effectiveness of several treatments with an infinite number of patients, it also provides a deterministic patient allocation rule that aims to optimize patient benefit on average. In order to do so, the rule must dynamically address the ethical conflict between learning (efficiency/power) and earning (patient benefit/ethics) after every patient is treated, its outcome observed and considering the potential outcomes of the future patients, given the observed history.The multi‐armed bandit problem and the Gittins index are based on a set of assumptions which may be restrictive when considered from a practical point of view (Villar et al., ). Particularly important assumptions include the infinite size of the trial, the observability of each patient's outcome before treating the next patient, and the lack of randomization of the resulting patient allocation rule. Any extensions of the original model that result from relaxing some (or all) of these assumptions would, in general, require either finding an appropriate extension of the Gittins index rule for the relaxed model (e.g., an index for the finite http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Covariate‐adjusted response‐adaptive randomization for multi‐arm clinical trials using a modified forward looking Gittins index rule

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12738
Publisher site
See Article on Publisher Site

Abstract

IntroductionThe Gittins index rule (Gittins and Jones, ) was developed as an optimal solution to the classic multi‐armed bandit problem. In the context of a clinical trial to test the effectiveness of several treatments with an infinite number of patients, it also provides a deterministic patient allocation rule that aims to optimize patient benefit on average. In order to do so, the rule must dynamically address the ethical conflict between learning (efficiency/power) and earning (patient benefit/ethics) after every patient is treated, its outcome observed and considering the potential outcomes of the future patients, given the observed history.The multi‐armed bandit problem and the Gittins index are based on a set of assumptions which may be restrictive when considered from a practical point of view (Villar et al., ). Particularly important assumptions include the infinite size of the trial, the observability of each patient's outcome before treating the next patient, and the lack of randomization of the resulting patient allocation rule. Any extensions of the original model that result from relaxing some (or all) of these assumptions would, in general, require either finding an appropriate extension of the Gittins index rule for the relaxed model (e.g., an index for the finite

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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