Predictors of early trial termination using individual-level participant data and aggregate-level data from multiple trials

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Background to the projectMaximising 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 attrition. 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 attrition (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 attrition 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 attrition.What the studentship will encompassRyan McChrystal is now undertaking a PhD project - using IPD for a range of trials - to further explore predictors of attrition 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 attrition.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 DP and additional advice from NW and SD. Building on a Cochrane review undertaken by KG, 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 attrition 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