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Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model

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  • Peijie Wang
  • Xingwei Tong
  • Shishun Zhao
  • Jianguo Sun

Abstract

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided.

Suggested Citation

  • Peijie Wang & Xingwei Tong & Shishun Zhao & Jianguo Sun, 2015. "Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(8), pages 1537-1551, April.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:8:p:1537-1551
    DOI: 10.1080/03610926.2014.944665
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    Cited by:

    1. P. G. Sankaran & S. Prasad, 2017. "An Additive Risks Regression Model For Middle-Censored Lifetime Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 459-479, September.
    2. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.

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