Causal Effects of Time-Dependent Treatment: Optimizing Care of Cancer Patients

描述(由申请人提供)：65岁及以上肺癌患者的替代治疗策略的有效性可以使用观察性医疗保健数据进行评估，例如SEER-Medicare。对观察数据的统计技术，如边际结构模型(MSM)，可以阐明时间依赖性治疗与患者生存之间的因果关系。在这个项目中，我们将MSM应用于SEER-Medicare数据，以研究在给定关于肿瘤特征、合并症以及人口和社会经济因素的个人信息的情况下，治疗(如手术、放射和/或化疗)对肺癌患者生存的因果影响。这个模型同时考虑了时间依赖的治疗和共病--两个通常相互关联的重要生存因素。然而，MSM仍然基于强烈的假设，如不存在与治疗分配和生存相关的未观察到的协变量，也不涉及疾病特定的临床信息。因此，我们在随机过程模型的最新进展的基础上，进一步发展了能够解决MSM局限性的广义方法，以更准确地评估研究效果。这项研究的结果将提供：i)在存在其他时间依赖协变量的情况下，对时间依赖治疗的因果推断理论的进一步洞察；ii)使用基于SAS的软件进行效应分析的新模型，该模型解决MSM的局限性；以及iii)治疗效果对肺癌分期的实质性结果 以及不同的合并症状态。该项目的成功完成将帮助临床医生根据个体患者的特点选择最佳的肺癌治疗方案，以平衡收益和风险。

DESCRIPTION (provided by applicant): The efficacy of alternative treatment strategies for lung cancer patients aged 65 years and older can be evaluated using observational heath care data such as SEER-Medicare. Statistical techniques for observational data such as marginal structural model (MSM) allow elucidating the causality between time- dependent treatments and patients' survival. In this project, we apply MSM to SEER-Medicare data to study the causal effects of treatment (such as surgery, radiation, and/or chemotherapy) on survival of lung cancer patients given individual information on tumor characteristics, comorbidities, and demographic and socio- economic factors. This model considers both time-dependent treatment and comorbidity--two important survival factors that are commonly interrelated. However, MSM is still based on strong assumptions such as the absence of unobserved covariates related to treatment assignment and survival, and also does not involve disease-specific clinical information. Therefore, we further develop the generalized approach based on recent stochastic process model advances which can address MSM limitations to more precisely evaluate studied effects. The results of this study will provide i) further insight into the theory of causal inferece of time- dependent treatment in the presence of other time dependent covariates such as comorbiditiy, ii) a new model for effects analysis with SAS-based software that addresses MSM limitations, and iii) substantive results of the treatment effects on lung cancer survival by stage and different comorbidity states. Successful completion of this project will assist clinicians in choosing the optimal lung cancer treatment that balances benefits and risks based on individual patient characteristics.