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

使用来自多个试验的个体水平参与者数据和聚合水平数据预测试验提前终止

• 批准号: 2833361
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2833361
• 关键词: Predictors; early; trial; termination; using

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

项目背景最大限度地保留是试验设计和实施的主要焦点，确定改善试验保留的策略已被确定为重要的研究优先事项。然而，我们还没有发现任何研究已经确定了哪些个体参与者特征预测了流失。这些知识将有助于为试验设计和实施提供信息。在初步工作中，使用行业资助的试验中的个人水平参与者数据（IPD），针对一系列指标条件和药物类别，我们发现合并症与自然减员相关（分析中使用的试验见https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1427-1）。每增加一种共病，损耗的比值比增加约1.1倍（比值比1.11，95% CI：1.07 - 1.14）。这在各种条件和药物类别之间高度一致。我们还发现，在试验中，年龄和性别并不能预测参与者的流失。学生身份将使Ryan McChrystal现在正在进行一个博士项目-使用IPD进行一系列试验-以进一步探索参与者流失的预测因素。例如，与指标疾病（如高血压和心绞痛）治疗相似的合并症可能不如治疗非常不同的合并症（如前列腺疾病和心力衰竭）重要。此外，其他因素（如多种用药、指数疾病严重程度、基线生活质量指标）也可能与损耗相关。除IPD外，分析还将包括试验水平数据。对于某些试验，FDA要求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、缺乏疗效、失访、医生决定、妊娠、方案违背，这些数据将与IPD一起在多水平Meta回归模型中进行分析（doi/10.1111/rssa.12579），在R/Stan包multinma（https：//github.com/dmphillippo/multinma）中实现。在这些模型中纳入聚合水平数据将增加样本量，提高精密度和普遍性，同时避免聚合偏倚和非线性偏倚。这些模型和它们实现的包都是新颖的，因此博士生将接受DP的培训以及NW和SD的额外建议。基于KG（监督团队成员）进行的科克伦综述（https：//www.cochranelibrary.com/cdsr/doi/10.1002/14651858.MR000032.pub3/full），学生将把他们的发现与随机试验中更广泛的保留文献联系起来。这将最大限度地发挥研究结果的效用，以便：-为试验设计提供信息，允许试验者预测不同资格标准对减员风险的影响，为试验的进行提供信息，确定哪些招募的受试者最有可能需要额外支持来完成试验，为未来的干预研究提供研究议程，特别是关于“保留不良”人群