权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Robust Statistical Methods to Identify Surrogate Markers in Diabetes

识别糖尿病替代标志物的稳健统计方法

基本信息

批准号：
8874218
负责人：
Layla Parast
金额：
$ 26.18万
依托单位：
RAND CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-01 至 2017-06-30
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：旨在确定有效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随访，以观察足够数量的糖尿病诊断，从而精确估计治疗效果。在这种情况下，可用于估计治疗效果并可在糖尿病诊断发生之前观察到的替代标记物的可用性将使研究人员能够在所需的随访时间更少的情况下得出关于治疗效果的结论。也就是说，经验证的替代标志物可以缩短随机临床试验的时间，并需要更小的样本量，从而加快临床信息的获取。识别这些替代标记物并确定何时应该收集这些标记物将对旨在识别糖尿病有效治疗的研究做出重大贡献。识别有用的替代标志物的研究重点是估计替代标志物解释的治疗效果比例，因为有效的替代标志物应捕获主要结局的大部分真实治疗效果。然而，目前的方法来估计解释的治疗效果的比例通常需要限制性的模型假设，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，通过提出允许更灵活的模型假设的新方法，使替代标记识别的研究实践远离限制性的基于模型的方法，转向稳健的估计方法。具体而言，我们建议开发新的统计方法来估计一个或多个潜在的替代标志物解释的治疗效果的比例，并确定何时应使用里程碑方法收集这些替代标志物信息，以便使用这些信息进行治疗有效性测试将具有理想的功效，从而减少所需的随访时间。我们将这些方法应用于糖尿病预防计划结局研究（DPPOS）的数据，以了解这些新方法是否可以识别糖尿病和糖尿病相关并发症的替代标志物，这些标志物可以充分捕获Metabolic和生活方式干预的预防性治疗效果，这两种预防形式在DPPOS中进行了检查。