13 citations · 45 across the 10 of their papers we have counts for
14 papers
Point estimation for adaptive trial designs II: practical considerations and guidance
David S. Robertson, Babak Choodari-Oskooei, Munya Dimairo +3
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As…
Online multiple hypothesis testing
David S. Robertson, James M. S. Wason, Aaditya Ramdas
Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as t…
Online FDR Control for RNAseq Data
Lathan Liou, Milena Hornburg, David S. Robertson
Motivation: While the analysis of a single RNA sequencing (RNAseq) dataset has been well described in the literature, modern research workflows often have additional complexity in…
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…
Familywise error rate control for block response-adaptive randomization
Ekkehard Glimm, David Robertson
Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order…
Online error control for platform trials
David S. Robertson, James M. S. Wason, Franz König +2
Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment co…
Point estimation for adaptive trial designs I: a methodological review
David S. Robertson, Babak Choodari-Oskooei, Munya Dimairo +3
Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value", and states that…
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…
Graphical approaches for the control of generalised error rates
David S. Robertson, James M. S. Wason, Frank Bretz
When simultaneously testing multiple hypotheses, the usual approach in the context of confirmatory clinical trials is to control the familywise error rate (FWER), which bounds the…
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…
Online control of the false discovery rate in biomedical research
David S. Robertson, James M. S. Wason
Modern biomedical research frequently involves testing multiple related hypotheses, while maintaining control over a suitable error rate. In many applications the false discovery r…
Design optimisation and post-trial analysis in group sequential stepped-wedge cluster randomised trials
Michael Grayling, David Robertson, James Wason +1
Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discus…
Familywise error control in multi-armed response-adaptive trials
David S. Robertson, James M. S. Wason
Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data, so that a greater proportion of patients ca…