This module will cover advanced statistical topics in clinical trials.

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

We will explore statistical methodology for early phase clinical trials (including model-based dose-finding designs such as the continual reassessment method (CRM) as well as single-arm trials).

Bayesian statistics, and their increasing application to aid forecasting and decision making in clinical trials will also be covered in the module.

In addition, workshops on communicating statistics to non-statisticians will be organised.        

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.

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

Conduct simulations and perform advanced sample size calculations to inform clinical trial design
	Conduct analyses in trials with advanced designs (e.g. group sequential, multi-arm multi stage)
	Perform Bayesian analyses of data to inform decision-making
	Discuss estimands and their application in clinical trials
	Select appropriate methods for handling missing data and apply them to clinical trial datasets
	Communicate advanced statistical concepts to non-statisticians by focusing on the key outputs of the methods, which they will learn to express in non-technical terms
This module is compulsory for students on the Statistics for Clinical Trials route of the MSc Clinical Trials.

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

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.

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

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

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.4067

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.1765

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/s12916-018-1017-7