School of Mathematical Sciences

A Bayesian framework for addressing informative missingness in the analysis of clinical trials menu

A Bayesian framework for addressing informative missingness in the analysis of clinical trials

Speaker: 
A. J. Mason, LSHTM
Date/Time: 
Thu, 11/01/2018 - 16:00
Room: 
Queens' W316
Seminar series: 

The analyses of randomised controlled trials (RCTs) with missing data typically assume that, after conditioning on the observed data, the probability of missing data does not depend on the patient's outcome, and so the data are ‘missing at random’ (MAR). This assumption is often questionable, for example because patients in relatively poor health may be more likely to drop-out. In these cases, methodological guidelines recommend sensitivity analyses to recognise data may be ‘missing not at random’ (MNAR), and call for the development of practical, accessible, approaches for exploring the robustness of conclusions to MNAR assumptions.

We propose a Bayesian framework for this setting, which includes a practical, accessible approach to sensitivity analysis and allows the analyst to draw on expert opinion. To facilitate the implementation of this strategy, we are developing a new web-based tool for eliciting expert opinion about outcome differences between patients with missing versus complete data. The IMPROVE study, a multicentre trial which compares endovascular strategy (EVAR) with open repair for patients with ruptured abdominal aortic aneurysm, was used in the initial development work. In this seminar, we will discuss our proposed framework and demonstrate our elicitation tool, using the IMPROVE trial for illustration.