Frailty Models and Survival Analysis in Cancer Research

描述（由申请人提供）：该提案的目标是为肿瘤学和其他健康科学慢性疾病的临床、流行病学和基础科学研究开发可行的生存分析工具。在Aim 1中，主要动机是艾滋病临床试验替代主要终点，如病毒失败可能因信息退出而被审查，天真的Cox模型分析可能具有误导性。我研究了代理端点的时间依赖回归模型和时间依赖依赖模型，包括保守敏感性分析，它解释了依赖审查。这些分析可以发现治疗效果和审查机制的细微时间变化，是艾滋病和其他慢性疾病研究中初始回归分析的有益补充，在这些研究中，退出是有问题的。在目标2中，我调查了多变量竞争风险数据的关联，这是基于人口的遗传流行病学生存研究的一个重要主题，如Cache县老龄化研究，其中家族发病与慢性疾病的关联，如痴呆，是感兴趣的。标准的审查数据关联分析不考虑发病年龄可能依赖于死亡审查。我将扩展经典的单变量分析的原因特异性危害和累积发生率函数，以获得新的时变关联措施和测试。这些方法将提供关于家族性疾病关联的基本知识，这可能是通过更简单的参数方法无法检测到的。在癌症试验中，比如在国家乳腺和肠外科辅助项目中，在测试协变量效应时经常使用特别的方法，其中一些转换被非正式地比较，多个测试问题可能被忽略。这种“作弊”增加了第一类错误率，并可能给出误导性的结果。目的3提出在制定生存终点的临床风险指数时，使用参数协变量转换对协变量进行最佳推断。构造的测试可能比具有固定转换的原始测试更强大。这些结果为癌症预后的探索性亚组分析提供了重要的指导。在Aim 4中，我研究了竞争风险数据的非参数分位数推理。累积发病率估计经常在癌症试验中报告，例如，联合放疗和化疗的局部复发率。非局部区域事件的依赖审查使分位数定义复杂化，分位数定义是生存分析中常用的摘要。独立审查数据的分位数广泛使用Kaplan-Meier曲线报告，但不适合竞争风险。提出的方法学将广泛适用于癌症应用，解决癌症研究中一个关键的方法学差距。对于每一个目标，用户友好的软件都将公开提供。主要科学期刊上的解释性论文将向具有高影响力的主题受众传播该方法。公共卫生相关性：该资助的目标是开发时间到事件终点的统计方法，该方法将广泛应用于肿瘤学的临床、流行病学和基础科学研究。这些方法将有助于识别家族和环境风险因素，这些因素对于正确评估未受影响个体未来的癌症风险以及对癌症患者制定有效的预防和治疗干预措施至关重要。目前的统计方法是不充分的，阻碍了研究的设计和分析，这些研究可能会改善癌症的预后和治疗。

DESCRIPTION (provided by applicant): The proposal's objective is to develop practicable survival analysis tools for clinical, epidemiologic, and basic science studies in oncology and other chronic diseases in the health sciences. In Aim 1, a main motivation is AIDS clinical trials surrogate primary endpoints, like viral failure may be censored by informative dropout and naive Cox model analyses may be misleading. I investigate time-dependent regression models for the surrogate endpoints and time-dependent dependence models, including conservative sensitivity analyses, which account for dependent censoring. The analyses may detect subtle temporal changes in treatment efficacy and the censoring mechanism and are useful complements to naive regression analyses in AIDS other chronic disease studies where dropout is problematic. In Aim 2, I investigate associations in multivariate competing risks data, an important topic in population based genetic epidemiologic survival studies, like the Cache County Study of Aging, where familial onset associations for chronic diseases, like dementia, are of interest. Standard censored data association analyses do not address that the onset ages may be dependently censored by death. I will extend classic univariate analyses of cause-specific hazard and cumulative incidence functions to obtain novel time-varying association measures and tests. The methods will provide fundamental knowledge about familial disease assocations which may not be detected by simpler parametric methods. In cancer trials, like those at the National Surgical Adjuvant Breast and Bowel Project, ad hoc approaches are often used when testing covariate effects, where a few transformations are compared informally and multiple testing issues may be ignored. Such "cheating" inflates the type I error rate and may give misleading results. Aim 3 proposes optimal inferences for covariates using parametric covariate transformations when developing clinical risk indices for survival endpoints. Tests are constructed which may be more powerful than naive tests with fixed transformations. These results provide critical guidance to analysts in exploratory subgroup analyses for cancer prognosis. In Aim 4, I study nonparametric quantile inference for competing risks data. Cumulative incidence estimates are often reported in cancer trials, for example, rates of locoregional recurrence with combined radiation and chemotherapy. The dependent censoring from non-locoregional events complicates quantile definition, a commonly used summary in survival analysis. Quantiles for independently censored data are widely reported using Kaplan-Meier curves, but are not appropriate with competing risks. The proposed methodology will be broadly applicable in cancer applications, addressing a key methodologic gap in cancer research. For each aim, user friendly software will made publicly avaiable. Expository papers in leading scientific journals will disseminate the methodology to high impact subject matter audiences. PUBLIC HEALTH RELEVANCE: The goal of this grant is to develop statistical methods for time-to-event endpoints which will be widely applicable in clinical, epidemiologic, and basic scientific research in oncology. The methods will be useful in identifying familial and environmental risk factors which are critical to correctly assessing future cancer risk in unaffected individuals and to developing effective preventive and therapeutic interventions in cancer patients. Current statistical methods are inadequate and have hindered the design and analysis of studies which could bring about improvements in cancer prognosis and treatment.