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
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ;
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud