

DART: Design and Analysis of Randomised Trials
Telephone number:Email Address: david.robertson@mrc-bsu.cam.ac.uk
I am a Biometrika Trust Research Fellow and Senior Research Associate based at the MRC Biostatistics Unit, University of Cambridge. My current research is on error rate control for modern clinical trial designs which test multiple hypotheses simultaneously. My main areas of focus include:
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- Trials where the multiple hypotheses tested have a natural (and possibly complex) grouping or ordering
- Designs which allow a potentially large number of new treatments to be added throughout the course of the trial
- Multi-arm trials where the probability of patients being randomised to the different treatment arms varies over time, based on the accumulated response data
Research Background
In the classical framework of drug development, the response to experimental therapies is evaluated one treatment at a time within a homogenous patient group. However, this paradigm is increasingly shifting towards testing multiple related hypotheses simultaneously. Examples include:- Multi-arm trials, which test multiple treatments in parallel
- Testing targeted therapies across multiple subgroups of patients, which is a key step towards the goal of personalised medicine
- Measuring multiple endpoints to help answer a range of clinical questions
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Selected Papers
Robertson DS, Wason JMS and Bretz F (2020)Graphical approaches for the control of generalised error ratesStatistics in Medicine 39 (23): 3135-3155
Wason JMS and Robertson DS (2020)Controlling type I error rates in multi‐arm clinical trials: A case for the false discovery rate
Pharmaceutical Statistics 75 (3): 885-894
Robertson DS and Wason JMS (2019)Familywise error control in multi-armed response-adaptive trials
Biometrics 75 (3): 885-894
Robertson DS, Wildenhain J, Javanmard A and Karp NA (2019)onlineFDR: an R package to control the false discovery rate for growing data repositories
Bioinformatics 35 (20): 4196-4199
Robertson DS, and Glimm E (2019)Conditionally unbiased estimation in the normal setting with unknown variances
Communications in Statistics - Theory and Methods 48 (3): 616-627
Robertson DS, Prevost AT, and Bowden J (2016)Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis-driven selection rules
Statistics in Medicine 35 (22): 3907-3922
Robertson DS, Prevost, AT and Bowden J (2016)Accounting for selection and correlation in the analysis of two-stage genome-wide association studies
Biostatistics 17 (4): 634-649
Robertson DS, Prevost AT and Bowden J (2015)Correcting for bias in the selection and validation of informative diagnostic tests
Statistics in Medicine 34 (8): 1417–1437