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<p>Propensity score weighting (PSW) is a valuable tool for estimating treatment effects on survival outcomes in observational studies. However, there is no clear best practice for applying PSW to complex survey data with survival outcomes. This paper addresses this gap by exploring how to integrate PSW into complex survey with design features (strata, clusters, sampling weights) for unbiased population-level estimates. We evaluate three PSW methods where: Method I: neither the propensity score (PS) model nor the outcome model accounts for the survey design; Method II: the PS model does not account for the survey design, but the outcome model does; Method III: both the PS model and outcome model account for the survey design. Through extensive simulations, we compare performance in estimating absolute treatment effects measured by population survival quantile effects and relative treatment effects measured by population marginal hazard ratios. Mean relative bias, mean absolute bias and coverage probability are estimated for model evaluations under various scenarios, including varying treatment effect magnitude, censoring type and rate, level of PS overlap, presence of outliers and nonresponse. Findings reveal that both survey-weighted Methods II and III outperform the unweighted Method I under most scenarios for both measures of treatment effects, especially when there is a true treatment effect. Both weighted methods II and III are found to perform closely, including when there exists informative censoring, influential outliers, or non-response. We recommend that when considering PSW with complex survey data for estimating population-level treatment effects on survival outcomes, both modelling stages should incorporate survey designs, but it is most critical for the outcome modelling. For illustration, all methods are applied to the public-use 2000–2018 National Health Interview Survey (NHIS) Linked to Mortality Files with mortality information through 2019 to estimate the effect of smoking cessation after a cancer diagnosis on subsequent overall survival.</p>

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Propensity score weighting analysis with complex survey data for estimating population-level treatment effects on survival: a simulation study

  • 期刊论文

  • 发表日期：

    2025年6月7日

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  • Lihua Li,
  • Chen Yang,
  • Wei Zhang,
  • Yulei He,
  • John R. Pleis,
  • Lauren M. Rossen,
  • Bian Liu,
  • Morgan Earp,
  • Madhu Mazumdar

摘要

Propensity score weighting (PSW) is a valuable tool for estimating treatment effects on survival outcomes in observational studies. However, there is no clear best practice for applying PSW to complex survey data with survival outcomes. This paper addresses this gap by exploring how to integrate PSW into complex survey with design features (strata, clusters, sampling weights) for unbiased population-level estimates. We evaluate three PSW methods where: Method I: neither the propensity score (PS) model nor the outcome model accounts for the survey design; Method II: the PS model does not account for the survey design, but the outcome model does; Method III: both the PS model and outcome model account for the survey design. Through extensive simulations, we compare performance in estimating absolute treatment effects measured by population survival quantile effects and relative treatment effects measured by population marginal hazard ratios. Mean relative bias, mean absolute bias and coverage probability are estimated for model evaluations under various scenarios, including varying treatment effect magnitude, censoring type and rate, level of PS overlap, presence of outliers and nonresponse. Findings reveal that both survey-weighted Methods II and III outperform the unweighted Method I under most scenarios for both measures of treatment effects, especially when there is a true treatment effect. Both weighted methods II and III are found to perform closely, including when there exists informative censoring, influential outliers, or non-response. We recommend that when considering PSW with complex survey data for estimating population-level treatment effects on survival outcomes, both modelling stages should incorporate survey designs, but it is most critical for the outcome modelling. For illustration, all methods are applied to the public-use 2000–2018 National Health Interview Survey (NHIS) Linked to Mortality Files with mortality information through 2019 to estimate the effect of smoking cessation after a cancer diagnosis on subsequent overall survival.