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

项目背景最大化保留是试验设计和实施的一个主要重点，确定改善试验保留的策略已被确定为重要的研究重点。然而，我们还不知道有任何研究已经确定了哪些个体参与者的特征可以预测人员流失。这些知识将有助于为试验设计和进行提供信息。在初步工作中，使用一系列指标条件和药物类别的行业资助试验的个人水平参与者数据，我们发现共病与消耗有关(有关分析中使用的试验，请参阅https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1427-1)。在额外的并存条件下，磨损的优势比增加了约1.1倍(优势比1.11，95%可信区间：1.07至1.14)。这在各种条件和药物类别中都是高度一致的。我们还发现，在试验中，年龄和性别并不能预测人员流失。学生资格将包括什么？瑞安·麦克里斯特尔目前正在进行一项博士项目--使用IPD进行一系列试验--以进一步探索参与人员减少的预测因素。例如，与指标性疾病治疗相似的并存疾病(如高血压和心绞痛)可能没有治疗方法非常不同的并存疾病(如前列腺癌和心力衰竭)那么重要。此外，其他因素(如多药、疾病严重程度指数、基线生活质量测量)也可能与磨损有关。除了IPD，分析还将包括试验水平的数据。对于某些试验，FDA要求在Clinicaltrials.gov上报告试验完成统计数据(以及未完成的原因，如不良事件、死亡、无效、失去随访、医生决定、怀孕、违反方案、按受试者或其他原因退出)。这些数据将与IPD一起在多水平元回归模型(DOI/10.1111/rssa.12579)中进行分析，该模型在R/STAN程序包Polymma(https://github.com/dmphillippo/multinma).)中实现在这些模型中包含聚集水平的数据将增加样本大小，提高精度和概括性，同时避免聚集偏差和不可折叠性偏差。这些模式和实施方案都是新颖的，因此博士生将接受DP的培训以及NW和SD的其他建议。KG是(https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.MR000032.pub3/full)，监督小组的一名成员，在科克伦评估的基础上，这名学生将把他们的发现与随机试验中关于保留的更广泛的文献联系起来。这将最大限度地发挥试验结果的效用：-通知试验设计，允许试验人员预测不同资格标准对消耗风险的影响通知试验进行，通过确定哪些招募的参与者最有可能需要额外支持来完成试验，为未来干预研究的研究议程提供信息，特别是关于“保留力差”的人群

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