Missing data occurs when data is unavailable to be analysed and is a common challenge within clinical trials that can have serious consequences for the validity of results. The analysis of trials with missing data usually assumes the missing data are “missing at random”, i.e. given an individual's past observed data, their probability of dropout does not depend on their present (or future) unobserved outcome (reference: https://bit.ly/307KGtW).
In many settings this assumption is implausible. For this reason, it is crucial to develop methods to assess the robustness of conclusions to departures from the missing at random assumption. Since we cannot base assumptions on data, an attractive approach is to incorporate experts’ opinions about reasons for and distributions of the missing data in the trial’s sensitivity analysis. In the past, experts have been defined as clinicians and methods to elicit their views have been developed. Patients have been overlooked in this process, even though they are likely to have important opinions to share regarding patient missing data. Currently, there is no method available to elicit patient’s views in this important aspect of trial analysis.
The current studentship aims to develop and test a practical, accessible approach that allows patient’s opinions about missing data in a clinical trial to be meaningfully and accurately elicited and incorporated into a trial’s sensitivity analyses.
The project involves:
1. Review of the literature on current expert elicitation methods available
2. Based on the findings from 1, co-design with a patient panel a tool to elicit their views on missing data. This would include a series of workshops led by the student to incorporate the panel’s views on what the new tool should look like, prioritise the key aspects to ensure feasibility and refine it.
3. Evaluate the tool developed in 2, by implementing it with a group of patients, using a real-world trial as an example, and based on criteria outlined by Johnson et al. for Bayesian elicitation tools (reference: https://bit.ly/30j3iHV) including validity, reliability, responsiveness, feasibility
4. Incorporate the opinions elicited in the application of the tool in (3) in a trial’s sensitivity analysis, using pre-established methods, to assess the robustness of the findings
5. Produce recommendations for the elicitation of patient’s views regarding missing data in clinical trials
Dr Beatriz Goulao will act as the lead supervisor. She is a statistician, trial methodologist and an expert in patient and public involvement in numerical aspects of trials based at the Health Services Research Unit (HSRU), University of Aberdeen. Co-supervisors include: Dr Lorna Aucott (senior statistician at HSRU, University of Aberdeen), Dr Tim Morris (methodologist and expert in methods to handle missing data in trials, UCL), Dr Lucy O’Malley (senior lecturer with expertise in health psychology, University of Manchester).
It will include collecting qualitative data from the co-design sessions, and collecting quantitative and qualitative data from the elicitation session.
The project will work closely with public partners to ensure the successful development and implementation of the tool. BG has an established network of public partners interested in the topic. HSRU/CHaRT and the University of Aberdeen also have access to established public partner’s groups that could support this work.
Applicants should hold a Master’s degree with a solid quantitative element, for example in psychology, health services research, or applied statistics.
HOW TO APPLY
You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:
Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.
Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.
Before making an application, applicants should contact the project primary supervisor to find out more about the project and to discuss their interests in the research.
The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.
Completed application forms must be returned to: [Email Address Removed]
Informal enquiries may be made to [Email Address Removed]