Robust Statistical Methods to Identify Surrogate Markers in Diabetes

描述(由申请人提供)：旨在确定有效的治疗和预防策略以降低糖尿病风险的临床试验通常需要对患者进行长期跟踪，以便观察足够数量的糖尿病诊断以准确估计治疗效果。在这种情况下，替代标记物的可用性可以用于评估治疗效果，并且可以在糖尿病诊断发生之前观察到，这将使研究人员能够在较少所需的后续时间内对治疗效果做出结论。也就是说，经过验证的替代标记可以实现更短的随机临床试验，并且需要更小的样本量，从而加快临床信息的获取。识别这种替代标记物并确定何时应该收集这些标记物将对旨在确定糖尿病有效治疗方法的研究做出重大贡献。寻找有用的替代标记的研究侧重于估计替代标记解释的治疗效果的比例，因为有效的替代标记应该捕捉到对主要结果的真实治疗效果的很大比例。然而，目前估计所解释的治疗效果比例的方法通常需要限制性的模型假设，而这些假设在实践中可能不成立。在这项研究中，我们的目标是转移电流 代理标记识别的研究实践从限制性的基于模型的方法转向稳健的估计方法，提出了允许更灵活的模型假设的新方法。具体地说，我们建议开发新的统计方法，以估计一个或多个潜在替代标记解释的治疗效果的比例，并确定何时应使用里程碑式的方法收集此类替代标记信息，以便使用该信息进行的治疗有效性测试将具有所需的力量，从而允许较少所需的后续时间。我们将把这些方法应用于糖尿病预防项目结果研究(DPPOS)的数据，以了解这些新方法是否能够识别糖尿病和与糖尿病相关的并发症的替代标记物，充分反映二甲双胍和生活方式干预的预防效果，这两种形式的预防在DPPOS中得到了检验。

DESCRIPTION (provided by applicant): Clinical trials aimed at identifying effective treatment and prevention strategies to reduce the risk of diabetes 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 treatment effect 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 and determining when these markers should be collected would contribute significantly to research aimed at identifying effective treatments in diabetes. Research on identifying useful surrogate markers focuses 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 usually require restrictive model assumptions that may not hold in practice. In this study, we aim to shift current research practice on surrogate marker identification away from restrictive model-based approaches towards a robust estimation approach 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 one or more potential surrogate markers and to identify when such surrogate marker information should be collected using a landmark approach such that a test for treatment effectiveness using the information will have desirable power, thereby allowing for less required follow-up time. We will apply these methods to data from the Diabetes Prevention Program Outcomes Study (DPPOS) in order to understand if such new methods can identify surrogate markers of diabetes and complications associated with diabetes that adequately capture the preventive treatment effect of Metformin and a lifestyle intervention, two forms of prevention examined in DPPOS.