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MRC Biostatistics Unit

Summary

I am a Senior Research Associate at the MRC Biostatistics Unit, University of Cambridge, where I have been based since 2013. My research focuses on the development of novel methodology for the design and analysis of adaptive clinical trials. From 2018 - 2021, I held a Biometrika Trust Research Fellowship, which explored questions around error rate control for clinical trial designs that test multiple hypotheses simultaneously. My main areas of research focus include:

  • Estimation after adaptive designs
  • Response-adaptive randomisation in clinical trials
  • Multiple hypothesis testing

Previously (from 2016-18) I was a Postdoctoral Statistician working with James Wason on the design and analysis of novel clinical trials. I did my PhD in the Biostatistics Unit under the supervision of Jack Bowden and Toby Prevost from 2013-16. My PhD project was on unbiased estimation in multi-arm drop-the-loser trials, with applications to diagnostic studies, genome-wide association studies and seamless phase II/III trials. Prior to this I did my undergraduate degree and masters in mathematics at the University of Cambridge.

Google Scholar profile
 

Selected Papers

Robertson DS, Lee KM, Lopez-Kolkovska BC and Villar SS (2023). Response-adaptive randomization in clinical trials: from myths to practical considerations. Statistical Science (with discussion), 38(2):185–208

Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T (2023). Point estimation for adaptive trial designs II: practical considerations and guidance. Statistics in Medicine, 42(14): 2496-2520.

Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T (2023). Point estimation for adaptive trial designs I: A methodological review. Statistics in Medicine, 42(2):122-145.

Robertson DS, Wason JMS, König F, Posch M, Jaki T (2023). Online error rate control for platform trials. Statistics in Medicine, 42(14), 2475-2495.

Robertson DS, Wason JMS, Ramdas A (2023). Online multiple hypothesis testing. Statistical Science, 38(4):557–575.

Villar SS, Robertson DS and Rosenberger WF (2021). The temptation of overgeneralizing response-adaptive randomization. Clinical Infectious Diseases 73(3): e842.

Wason JMS and Robertson DS (2021). Controlling type I error rates in multi‐arm clinical trials: A case for the false discovery rate. Pharmaceutical Statistics 20(1), 109-116.

Robertson DS, Wason JMS and Bretz F (2020). Graphical approaches for the control of generalised error rates. Statistics in Medicine 39(23): 3135-3155.

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, 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.

 

Software

onlineFDR: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.