Causal inference for left-truncated and right-censored data with covariate measurement error

被引：0
作者：
Li-Pang Chen
机构：
[1] Western University,Department of Statistical and Actuarial Sciences
来源：
Computational and Applied Mathematics | 2020年 / 39卷
关键词：
Causal; Left-truncation; Measurement error; Propensity score; Right-censoring; Transformation model; 62N01; 62N02; 62E20; 62F12;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Causal inference is an important tool in observational studies. Many estimation procedures have been developed under complete data and precise measurements. However, when the datasets contain the incomplete responses induced by right-censoring and the covariate subject to measurement error, little work has been available to simultaneously address these features. Moreover, prevalent sampling is also a frequent phenomenon in survival analysis, and it makes analysis challenging since prevalent sampling causes a biased sample. In this paper, we are interested in exploring the causal estimation with those complex features incorporated. We propose the valid estimation procedure to estimate the average causal effect and the survivor functions based on different treatment assignments. Theoretical results of the proposed method are also established. Numerical studies are reported to assess the performance of the proposed method.
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  • [1] Andersen PK(2017)Causal inference in survival analysis using pseudo-observations Stat Med 36 2669-2681
  • [2] Syriopoulou E(2002)Semiparametric analysis of transformation models with censored data Biometrika 89 659-668
  • [3] Parner ET(2018)Semiparametric estimation for the accelerated failure time model with length-biased sampling and covariate measurement error Stat 7 e209-148
  • [4] Chen K(2019)Pseudo likelihood estimation for the additive hazards model with data subject to left-truncation and right-censoring Stat Interface 12 135-442
  • [5] Jin Z(2019)Semiparametric estimation for cure survival model with left-truncated and right-censored data and covariate measurement error Stat Probab Lett 154 108547-1038
  • [6] Ying Z(2020)Semiparametric estimation for the transformation model with length-biased data and covariate measurement error J Stat Comput Simul 90 420-716
  • [7] Chen L-P(2001)Causal inference on the difference of the restricted mean lifetime between two groups Biometrics 57 1030-1328
  • [8] Chen L-P(2012)Estimating propensity scores and causal survival functions using prevalent survival data Biometrics 68 707-300
  • [9] Chen L-P(1994)Simulation-extrapolation estimation in parametric measurement error models J Am Stat Assoc 89 1314-227
  • [10] Chen L-P(1965)The environment and disease: association or causation? Proc R Soc Med 58 295-2960
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