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Patrick Royston, Senior Statistician

Patrick Royston joined the MRC Clinical Trials Unit in 2000, having previously been a professor in the Department of Statistics and Evaluation, Imperial College, London, UK. He has been a senior scientist (statistician) at the MRC Clinical Trials Unit (since August 2013, MRC Clinical Trials Unit at UCL, part of the UCL Institute of Clinical Trials and Methodology). He worked originally on cancer trials and statistical methodology and latterly has focused more on methodology, while retaining a special interest in cancer trials. Patrick is a Professor of Statistics at University College London.

Patrick’s interests centre on statistical modelling and its medical applications, modelling continuous predictors in observational studies, survival analysis, and methodology of clinical trial design and analysis. A recent ongoing interest is the design and analysis of trials with possible non-proportional hazards of the treatment effect. He has a strong interest in developing and disseminating software implementation of new research methods in the form of Stata programs. He was recently awarded the 2016 Stata Journal Editors' prize for his contributions. He has co-authored two books; Multivariable model-building. A pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables (2008) with Willi Sauerbrei of Freiburg University, Germany and Flexible parametric survival analysis using Stata: Beyond the Cox model (2011) with Paul Lambert of Leicester University, UK. He has also collaborated with Douglas G Altman (Oxford University) on a variety of projects over many years.

 

Selected publications (2013-2016):

Choodari-Oskooei B, Parmar MKB, Royston P, Bowden J (2013) Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multi-stage (TAMS) trials with time to event outcome. Trials 14: 23.

Royston P, Altman DG (2013) External validation of a Cox prognostic model: principles and methods. BMC Medical Research Methodology 13: 33; doi: 10.1186/1471-2288-13-33.

Royston P, Parmar MKB (2013) Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Medical Research Methodology 13: 152; http://www.biomedcentral.com/1471-2288/13/152.

Kasenda B, Sauerbrei W, Royston P, Briel M (2014) Investigation of continuous effect modifiers in a meta-analysis on higher versus lower PEEP in patients requiring mechanical ventilation - protocol of the ICEM study. Systematic Reviews 3: 46.

Royston P, Parmar MKB (2014) An approach to trial design in the era of non-proportional hazards of the treatment effect. Trials 15: 314. doi: 10.1186/1745-6215-15-314.

Bratton DJ, Choodari-Oskooei B, Royston P (2015) A menu-driven facility for sample-size calculation in multi-arm multi-stage randomised controlled trials with time-to-event outcomes: Update. Stata Journal 15: 350-368.

Jinks RC, Royston P, Parmar MKB (2015) Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data. BMC Medical Research Methodology 15:82. doi 10.1186/s12874-015-0078-y.

Royston P (2015) Tools for checking calibration of a Cox model in external validation: Graphical approach based on risk groups. Stata Journal 15: 275-291.

Royston P (2015) Estimating the treatment effect in a clinical trial using difference in restricted mean survival time. Stata Journal 15: 1098-1117.

Royston P, Parmar MKB (2016) Augmenting the logrank test in the design of clinical trials in which non-proportional hazards of the treatment effect may be anticipated. BMC Medical Research Methodology 16:16. doi 10.1186/s12874-016-0110-x.

 

A full list of Patrick's publications is available here.

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Research interests:

  • Statistical modelling and its medical applications
  • Modelling continuous predictors
  • Survival analysis
  • Methodology of clinical trial design
  • Imputation of missing covariate data

Research areas:

 

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