Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

Lin, Jingyi; Trinquart, Ludovic

doi:10.1177/09622802221102625

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…First, we assume that there is no unmeasured confounding; However, there are often confounding variables that cannot be measured in observational studies, which leads to a biased estimation [ 23 , 24 ]. Second, our proposed method could not deal with competing risks, which are also common in survival data [ 25 , 26 ]. Third, we identify the candidate models based on the significance level of 0.05 and 0.1 in the empirical study, while the model specification may need clinical knowledge and existing literature, or statistical methods [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

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

Wang,

Wei,

Huang

et al. 2023

BMC Med Res Methodol

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Background When 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. Method Based 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. Results The 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. Conclusions The 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.

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Section: Discussionmentioning

confidence: 99%

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

Wang,

Wei,

Huang

et al. 2023

BMC Med Res Methodol

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causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data

Vakulenko-Lagun,

Magdamo,

Charpignon

et al. 2023

Computer Methods and Programs in Biomedicine

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Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time

Šinkovec,

Gall,

Heinze

2024

Medical Care

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Background: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time. Methods: For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databases, we propose using restricted mean survival time or restricted mean time lost, which are applicable even in competing risk situations. We demonstrate their application by assessing the compliance of patients with acute myocardial infarction (AMI) to high-power statins over 12 months in Austria’s political districts, using pseudo-observations and employing causal inference methods to achieve regional comparability. Results: We analyzed the compliance of 31,678 AMI patients from Austria’s 116 political districts with index AMI between 2011 and 2015. The results revealed considerable compliance variations across districts but also plausible spatial similarities. Conclusions: Restricted mean survival time and restricted mean time lost provide interpretable estimates of patients’ expected time in compliance (lost), well-suited for risk-adjusted entity comparisons in the presence of (measurable) confounding, censoring, and competing risks.

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Doubly-robust estimator of the difference in restricted mean times lost with competing risks data

Cited by 4 publications

References 56 publications

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

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

causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data

Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time

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