13 citations · 20 across the 4 of their papers we have counts for
4 papers
Some performance considerations when using multi-armed bandit algorithms in the presence of missing data
Xijin Chen, Kim May Lee, Sofia S. Villar +1
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 t…
Conditional Power and Friends: The Why and How of (Un)planned, Unblinded Sample Size Recalculations in Confirmatory Trials
Kevin Kunzmann, Michael J. Grayling, Kim M. Lee +3
Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Designs with adaptive sample size need to acco…
A review of Bayesian perspectives on sample size derivation for confirmatory trials
Kevin Kunzmann, Michael J. Grayling, Kim May Lee +3
Sample size derivation is a crucial element of the planning phase of any confirmatory trial. A sample size is typically derived based on constraints on the maximal acceptable type…
Response-adaptive randomization in clinical trials: from myths to practical considerations
David S. Robertson, Kim May Lee, Boryana C. Lopez-Kolkovska +1
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In t…