Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.

ABSTRACT: When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting o

SUBMITTER: Chen X 

PROVIDER: S-EPMC9467360 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature