Multiply robust estimator for the difference in survival functions using pseudo-observations

BMC Medical Research Methodology

Research

Kecheng Wei¹  Ce Wang¹  Chen Huang¹  Yongfu Yu²  Guoyou Qin² 

[1] Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China;Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China;Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China;

关键词: Survival function;    Survival outcome;    Empirical likelihood;    Propensity score;    Multiply robust;   

DOI  :  10.1186/s12874-023-02065-6

 received in 2023-02-01, accepted in 2023-10-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundWhen estimating the causal effect on survival outcomes in observational studies, it is necessary to adjust confounding factors due to unbalanced covariates between treatment and control groups. There is no study on multiple robust method for estimating the difference in survival functions. In this study, we propose a multiply robust (MR) estimator, allowing multiple propensity score models and outcome regression models, to provide multiple protection.MethodBased on the previous MR estimator (Han 2014) and pseudo-observation approach, we proposed a new MR estimator for estimating the difference in survival functions. The proposed MR estimator based on the pseudo-observation approach has several advantages. First, the proposed estimator has a small bias when any PS and OR models were correctly specified. Second, the proposed estimator considers the advantage pf the pseudo-observation approach, which avoids proportional hazards assumption. A Monte Carlo simulation study was performed to evaluate the performance of the proposed estimator. And the proposed estimator was used to estimate the effect of chemotherapy on triple-negative breast cancer (TNBC) in real data.ResultsThe simulation studies showed that the bias of the proposed estimator was small, and the coverage rate was close to 95% when any model for propensity score or outcome regression is correctly specified regardless of whether the proportional hazard assumption holds, finite sample size and censoring rate. And the simulation results also showed that even though the propensity score models are misspecified, the bias of the proposed estimator was still small when there is a correct model in candidate outcome regression models. And we applied the proposed estimator in real data, finding that chemotherapy could improve the prognosis of TNBC.ConclusionsThe proposed estimator, allowing multiple propensity score and outcome regression models, provides multiple protection for estimating the difference in survival functions.The proposed estimator provided a new choice when researchers have a "difficult time" choosing only one model for their studies.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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