A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates

FENG Fan, ZHAO Shishun, LI Shuwei, SUN Jianguo

Journal of Systems Science & Complexity ›› 2024, Vol. 37 ›› Issue (6) : 2721-2737.

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Journal of Systems Science & Complexity ›› 2024, Vol. 37 ›› Issue (6) : 2721-2737. DOI: 10.1007/s11424-024-3549-6

A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates

  • FENG Fan1, ZHAO Shishun2, LI Shuwei3, SUN Jianguo4
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Abstract

Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys. In addition, some covariates may be subject to measurement errors due to the instrumental contamination, biological variation or other reasons. For the analysis of interval-censored data with mis-measured covariates, the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate, which both have obvious limitations. To overcome these shortcomings, the authors propose a simulation-extrapolation estimation procedure under a general class of transformation models. The resulting estimators are shown to be consistent and asymptotically normal. The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in practice. In particular, the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account. Finally, the proposed method is applied to a real data set on hypobaric decompression sickness.

Key words

Interval censoring / measurement error / regression analysis / semiparametric model / SIMEX

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FENG Fan , ZHAO Shishun , LI Shuwei , SUN Jianguo. A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates. Journal of Systems Science & Complexity, 2024, 37(6): 2721-2737 https://doi.org/10.1007/s11424-024-3549-6

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Funding

This research was supported by the National Statistical Science Research Project under Grant No. 2022LY041, and the Nature Science Foundation of Guangdong Province of China under Grant No. 2021A1515010044, and the National Nature Science Foundation of China under Grant No. 12071176.
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