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疗效可以通过观察性医疗保健数据（如SEER-Medicare）进行评估。观察数据的统计技术，如边际结构模型（MSM），允许阐明时间依赖性治疗和患者生存之间的因果关系。在这个项目中，我们将MSM应用到SEER-Medicare数据中，研究治疗（如手术、放疗和/或化疗）对肺癌患者生存的因果影响，并给出肿瘤特征、合并症、人口统计学和社会经济因素的个体信息。该模型考虑了时间依赖性治疗和合并症——两个通常相互关联的重要生存因素。然而，男男性行为仍然基于强有力的假设，如缺乏与治疗分配和生存相关的未观察到的协变量，也不涉及疾病特异性临床信息。因此，我们在最近随机过程模型进展的基础上进一步发展广义方法，以解决MSM的局限性，从而更精确地评估所研究的效果。本研究的结果将提供i)在存在其他时间相关协变量（如合并症）的情况下，进一步了解时间依赖治疗的因果推论理论，ii)使用基于sas的软件进行效果分析的新模型，以解决MSM的局限性，以及iii)治疗对肺癌分期生存的实质性结果

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.