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Estimation of complier causal treatment effects with informatively interval-censored failure time data

Author

Listed:
  • Yuqing Ma

    (Jilin University)

  • Peijie Wang

    (Jilin University)

  • Jianguo Sun

    (University of Missouri)

Abstract

Estimation of compiler causal treatment effects has been discussed by many authors under different situations but only limited literature exists for interval-censored failure time data, which often occur in many areas such as longitudinal or periodical follow-up studies. Particularly it does not seem to exist a method that can deal with informative interval censoring, which can happen naturally and make the analysis much more challenging. Also, it has been shown that when the informative censoring exists, the analysis without taking it into account would yield biased or misleading results. To address this, we propose an estimated sieve maximum likelihood approach with the use of instrumental variables. The asymptotic properties of the resulting estimators of regression parameters are established, and a simulation study is performed and suggests that it works well. Finally, it is applied to a set of real data that motivated this study.

Suggested Citation

  • Yuqing Ma & Peijie Wang & Jianguo Sun, 2023. "Estimation of complier causal treatment effects with informatively interval-censored failure time data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(6), pages 1039-1062, December.
    • Handle: RePEc:spr:aistmt:v:75:y:2023:i:6:d:10.1007_s10463-023-00874-6
    DOI: 10.1007/s10463-023-00874-6
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    References listed on IDEAS

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    1. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    2. Chen, Xiaohong & Fan, Yanqin & Tsyrennikov, Viktor, 2006. "Efficient Estimation of Semiparametric Multivariate Copula Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1228-1240, September.
    3. Ma, Ling & Hu, Tao & Sun, Jianguo, 2016. "Cox regression analysis of dependent interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 79-90.
    4. Wen Yu & Kani Chen & Michael E. Sobel & Zhiliang Ying, 2015. "Semiparametric transformation models for causal inference in time-to-event studies with all-or-nothing compliance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 397-415, March.
    5. Donglin Zeng & Qingxia Chen & Ming-Hui Chen & Joseph G. Ibrahim, 2012. "Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study," Biometrika, Biometrika Trust, vol. 99(1), pages 167-184.
    6. Xuelin Huang & Robert A. Wolfe, 2002. "A Frailty Model for Informative Censoring," Biometrics, The International Biometric Society, vol. 58(3), pages 510-520, September.
    7. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
    8. Jack Cuzick & Peter Sasieni & Jonathan Myles & Jonathan Tyrer, 2007. "Estimating the effect of treatment in a proportional hazards model in the presence of non‐compliance and contamination," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 565-588, September.
    9. J. Sun, 1999. "A nonparametric test for current status data with unequal censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 243-250.
    10. Jing Cheng & Dylan S. Small & Zhiqiang Tan & Thomas R. Ten Have, 2009. "Efficient nonparametric estimation of causal effects in randomized trials with noncompliance," Biometrika, Biometrika Trust, vol. 96(1), pages 19-36.
    11. Jared D. Huling & Menggang Yu & A. James O'Malley, 2019. "Instrumental variable based estimation under the semiparametric accelerated failure time model," Biometrics, The International Biometric Society, vol. 75(2), pages 516-527, June.
    12. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    13. Hui Nie & Jing Cheng & Dylan S. Small, 2011. "Inference for the Effect of Treatment on Survival Probability in Randomized Trials with Noncompliance and Administrative Censoring," Biometrics, The International Biometric Society, vol. 67(4), pages 1397-1405, December.
    14. Ling Ma & Tao Hu & Jianguo Sun, 2015. "Sieve maximum likelihood regression analysis of dependent current status data," Biometrika, Biometrika Trust, vol. 102(3), pages 731-738.
    15. Mingyue Du & Qingning Zhou & Shishun Zhao & Jianguo Sun, 2021. "Regression analysis of case-cohort studies in the presence of dependent interval censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(5), pages 846-865, April.
    16. Shuli Li & Robert J. Gray, 2016. "Estimating treatment effect in a proportional hazards model in randomized clinical trials with all-or-nothing compliance," Biometrics, The International Biometric Society, vol. 72(3), pages 742-750, September.
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