Robust Statistical Methods to Identify and Use Surrogate Markers in Diabetes

项目摘要/摘要 糖尿病预防研究通常需要对患者进行长期随访，以观察适当的fi数值 糖尿病的诊断，以准确地估计治疗效果。在这种情况下，代理的可用性 可用于评估治疗效果的标记，可在发生 糖尿病诊断将允许研究人员对特定治疗的有效性做出结论。 所需的随访时间更短。也就是说，经过验证的替代标记物可以实现更短的随机临床 而且需要较小的样本量，从而加快了临床信息的获取。确定这样的 代理标记，确定何时应该收集这些标记，并开发使用这些标记的工具 在未来的研究中测试一种治疗是否有效的标记物将对有针对性的研究做出重大贡献 在寻找有效的糖尿病预防治疗方面。 识别有用的替代标记的研究主要集中在治疗比例的估计上 效果由代理项标记解释，因为有效的代理项标记应捕获很大比例的真 治疗效果对主要结局的影响。然而，目前估计治疗效果比例的方法 所解释的有一些局限性。特别是，它们通常需要限制性的模型假设，这些假设可能 在实践中不成立，它们通常只允许在单个替代标记上测量的单个替代标记的评估 时间点。此外，目前的方法没有提供关于如何实际使用Identifi边缘的任何指导 有效的替代标记物，用于在未来研究中更早地测试治疗效果。在这项研究中，我们的目标是转移电流 替代标记评价摆脱基于模型的限制性方法走向稳健性的研究实践 通过提出新的方法来评估复杂替代标记信息的估计方法 这允许更多的fl可伸缩模型假设。具体地说，我们建议开发新的统计方法 估计替代标记物随时间和时间的变化所解释的治疗效果的比例 多个代理标记，并确定如何使用这些代理标记信息来测试治疗 未来研究的有效性，从而减少了所需的后续时间和试验时间。此外，我们还 建议开发利用替代标记来识别异质性的方法和程序，以 在未来的研究中，当使用替代标记来测试治疗效果时，应考虑这种异质性。 我们将把这些方法应用于糖尿病预防计划研究的数据，以综合评估 并识别潜在的糖尿病替代标记物，并生产工具，使得IDENTIfi识别替代标记物 可用于在未来的糖尿病研究中测试有效的治疗方法。

Project Summary/Abstract Diabetes prevention studies often require long term follow-up of patients in order to observe a sufficient number of diabetes diagnoses to precisely estimate treatment effects. In such settings, the availability of a surrogate marker that could be used to estimate the treatment effect and could be observed earlier than the occurrence of a diabetes diagnosis would allow researchers to make conclusions regarding the effectiveness of a given treatment with less required follow-up time. That is, validated surrogate markers could enable shorter randomized clinical trials and require smaller sample sizes, thus accelerating acquisition of clinical information. Identifying such surrogate markers, determining when these markers should be collected, and developing tools to use these markers to test whether a treatment is effective in future studies would contribute significantly to research aimed at identifying effective preventative treatments for diabetes. Research on identifying useful surrogate markers has largely focused on estimation of the proportion of treatment effect explained by a surrogate marker since a valid surrogate marker should capture a large proportion of the true treatment effect on the primary outcome. However, current methods to estimate the proportion of treatment effect explained have a number of limitations. In particular, they often require restrictive model assumptions that may not hold in practice and they often only allow for the evaluation of single surrogate marker measured at a single point in time. In addition, current methods do not provide any guidance regarding how to actually use an identified valid surrogate marker to test for a treatment effect earlier in a future study. In this study, we aim to shift current research practice on surrogate marker evaluation away from restrictive model-based approaches towards robust estimation approaches that can evaluate complex surrogate marker information by proposing novel methods that allow for more flexible model assumptions. Specifically, we propose to develop novel statistical methods to estimate the proportion of treatment effect explained by surrogate marker measurements over time and by multiple surrogate markers, and identify how such surrogate marker information can be used to test for treatment effectiveness in a future study, thereby allowing for less required follow-up time and shorter trials. We additionally propose to develop methods to identify heterogeneity in the utility of a surrogate marker and a procedure to account for such heterogeneity when using the surrogate marker to test for a treatment effect in a future study. We will apply these methods to data from the Diabetes Prevention Program study to comprehensively evaluate and identify potential surrogate markers of diabetes and to produce tools such that identified surrogate markers could be used to test for effective treatments in future diabetes studies.