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This module will cover advanced statistical topics in clinical trials.Ìý

Special attention will be given to the role of estimands in clinical trials, as well as the related topic of missing data.Ìý

Some complex trial designs (e.g. factorial trials, non-inferiority trials, longitudinal cluster randomised trials, trials with longitudinal data) will be revisited or introduced, and considerations related to estimands, sample size calculations and estimation will be covered in depth.ÌýÌý

The principles of sample size calculations will be studied in detail, including sample size calculations for trials with continuous, binary, and time-to-event outcomes. The use of simulation studies in designing and analysing trials in general, and more specifically in performing sample size calculations, will be discussed.Ìý

Novel late phase clinical trials designs (e.g. group sequential and Multi-Arm Multi-Stage (MAMS)) will be studied in detail with respect to their statistical properties.ÌýÌý

The aim of this module is to provide you with an in-depth understanding of advanced topics that statisticians face in clinical trials. You will learn the theory that supports advanced designs and analysis techniques in clinical trials and learn how to perform key statistical tasks that statisticians carry out on clinical trials, in the public and private sectors.Ìý

At the end of the module, you will be able to:Ìý

1. Discuss estimands and their application in clinical trialsÌý

2. Select appropriate methods for handling missing data and apply them to clinical trial datasetsÌý

3. Conduct analyses in trials with advanced designsÌýÌý

4. Conduct simulations and perform advanced sample size calculations to inform clinical trial designÌý

This module is compulsory for students on the Statistics for Clinical Trials route of the MSc Clinical Trials.Ìý

Suess, Eric A., and Bruce E. Trumbo. Introduction to probability simulation and Gibbs sampling with R. Springer Science & Business Media, 2010.Ìý

Lunn D, Jackson C, Best N, Thomas A, Spiegelhalter D. The BUGS Book: A Practical introduction to Bayesian analysis. Boca Raton; London: CRC Press; 2013.Ìý

Morris TP, White IR, Crowther MJ. ‘Using simulation studies to evaluate statistical methods’. Stat Med. 2019; 38:2074–2102. doi:10.1002/sim.8086Ìý

Carpenter JR, Kenward MG. Multiple Imputation and Its Application. 1st ed. Chichester, West Sussex: John Wiley & Sons, Ltd.; 2013.Ìý

White IR, Royston P, Wood AM. ‘Multiple imputation using chained equations: issues and guidance for practice’. Stat Med. 2011; 30:377-399. doi:10.1002/sim.4067Ìý

Mallinckrodt C, Molenberghs G, Rathmann S. ‘Choosing estimands in clinical trials with missing data’. Pharm Stat. 2017;16(1):29-36. doi:10.1002/pst.1765Ìý

Pallmann P, Bedding AW, Choodari-Oskooei B, et al. ‘Adaptive designs in clinical trials: why use them, and how to run and report them’. BMC Med. 2018;16(1):1-15. doi:10.1186/s12916-018-1017-7Ìý

Choodari-Oskooei, et al. Multi-arm multi-stage (MAMS) platform randomized clinical trials. In: Principles and Practice of Clinical Trials. Springer; 2022. it.ly/3tmx0qTÌý