Abstract：
Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women\'s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.
摘要：
在存在竞争风险的情况下，研究与临床结果相关的治疗或暴露的数据分析方法历史悠久，通常具有假设的推理目标，因此需要对可用数据的可识别性进行强有力的假设。这里的数据分析方法被认为是基于单一和更高维的边际危险率，在标准独立审查假设下可识别的数量。这些自然导致联合生存功能估计器对感兴趣的结果，包括相互竞争的风险结果，为解决各种数据分析问题提供依据。这些方法将使用模拟和妇女健康倡议队列和临床试验数据集进行说明，和额外的研究需求将被描述。
