Background to the project
Maximising retainment is a major focus of trial design and conduct and identifying strategies to improve trial retention has been identified as an important research priority. However, we are not aware of any study which has identified which individual participant characteristics predict early termination. Such knowledge would be useful for informing both trial design and conduct.
In preliminary work, using individual-level participant data (IPD) across industry-funded trials for a range of index conditions and drug classes we found that comorbidity was associated with early termination (see https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1427-1 for trials used in the analysis). Per additional comorbid condition, the odds ratio for early termination increased by around 1.1-fold (odds ratio 1.11, 95% CI:1.07 to 1.14). This was highly consistent across conditions and drug classes. We also found that within trials age and sex did not predict early termination.
What the studentship will encompass:
We now propose that a PhD project be undertaken - using IPD for a range of trials - to further explore predictors of early termination of participation. For example, comorbid conditions which have similar treatments to the index condition (eg hypertension and angina) may be less important than comorbid conditions with very different treatments (eg prostate disease and heart failure). Moreover, other factors (eg polypharmacy, index disease severity, baseline quality of life measures) may also be associated with early termination.
In addition to IPD, the analysis will also include trial-level data. For certain trials the FDA mandates reporting of trial completion statistics (alongside the reasons for non-completion such as adverse event, death, lack of efficacy, lost to follow-up, physician decision, pregnancy, protocol violation, withdrawal by subject or other) on clinicaltrials.gov. These data will be analysed alongside the IPD in multi-level meta regression models (doi/10.1111/rssa.12579) as implemented in the R/Stan package multinma (https://github.com/dmphillippo/multinma). The inclusion of aggregate-level data in these models will increase the sample size, improving precision and generalisability whilst avoiding aggregation bias and non-collapsibility bias. The models and the package in which they are implemented are both novel, so the PhD student will receive training in both from Dr David Phillippo and additional advice from Dr Nicky Welton and Dr Sofia Dias.
Building on a Cochrane review undertaken by Dr Katie Gillies, a member of the supervisory team (https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.MR000032.pub3/full), the student will relate their findings to the wider literature on retention in randomised trials. This will maximise the utility of the findings for: -
· informing trial design, by allowing trialists to predict the effect of different eligibility criteria on the risk of early termination
· informing trial conduct, by identifying which recruited participants are at most likely to require additional support to complete a trial
· informing the research agenda for future intervention studies, particularly as regards “retention poor” populations
Dr David McAllister is a Wellcome Intermediate Clinical Fellow and professor with expertise in the secondary analysis of routine healthcare data and trial data.
Rod Taylor is Chair of Population Health Research, his methodological expertise is clinical trial design and he is Chief Investigator/co-applicant on a number of NIHR, CSO, MRC and Wellcome funded clinical trials.
Katie Gillies is Director of the Health Care Assessment Programme and Reader working in Methodological Research related to participant centred trials. She co-leads the MRC-NIHR TMRP Trial Conduct Working Group.
David Phillippo holds an MRC postdoctoral Fellowship. He has developed novel statistical methods to analyse IPD and aggregate level trial data. His current MRC postdoctoral fellowship will develop these methods further.
The PhD supervisory team will train the student in trial methodology and in the secondary analysis of individual-level trial data and aggregate level data and regression modelling. The team will also guide the student in identifying suitable participant characteristics for inclusion in the modelling and will help them consider how this knowledge might be operationalised in trial design and conduct. As well as the core supervisory team, Professors Nicky Welton and Sofia Dias, will act as an advisory team for the multi-level meta regression models.
There will be no primary data collection. The work will involve analysis of data within the CSDR, Vivli and YODA trial platforms using trial IPD. It will also involve accessing data from the clincialtrials.gov register via the AACT platform (https://aact.ctti-clinicaltrials.org/). The supervisory team have previously published work using both of these data resources.
Candidates should be familiar with regression modelling. The existing code base is in R, and this can be used within the trial platforms so we the student should either be skilled in R, or should be have expertise in one or more other similar languages (e.g. python) which would suggest the candidate could quickly acquire skills in R.
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]