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Nonparametric Estimation of Sojourn Time Distributions for Truncated Serial Event Data—a Weight-adjusted Approach

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Abstract

In follow-up studies, survival data often include subjects who have had a certain event at recruitment and may potentially experience a series of subsequent events during the follow-up period. This kind of survival data collected under a cross-sectional sampling criterion is called truncated serial event data. The outcome variables of interest in this paper are serial sojourn times between successive events. To analyze the sojourn times in truncated serial event data, we need to confront two potential sampling biases arising simultaneously from a sampling criterion and induced informative censoring. In this study, nonparametric estimation of the joint probability function of serial sojourn times is developed by using inverse probabilities of the truncation and censoring times as weight functions to accommodate these two sampling biases under various situations of truncation and censoring. Relevant statistical properties of the proposed estimators are also discussed. Simulation studies and two real data are presented to illustrate the proposed methods.

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References

  • Fleming TR, Harrington DP (1991) Counting processes and survival analysis. Wiley, New York

    Google Scholar 

  • Chang S-H, Tzeng S-J (2005) Nonparametric estimation for serial sojourn times under random truncation, Techincal Report, Division of Biostat., Institute of Epi., College of Public Health, National Taiwan University

  • Chen THH, Chiu YC, Luh DL, Yen MF, Wu HM, Chen LS, Taiwan Community-based Integrated Screening Group (2004) Community-based multiple screening model: design, implementation, and analysis of 42,387 participants. Cancer 100:1734–1743

    Google Scholar 

  • Clayton DG (1987) A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65:141–151

    MathSciNet  Google Scholar 

  • Gurler U (1996) Bivariate estimation with right-truncated data. J Amer Stat Assoc 91:1152–1165

    MATH  Google Scholar 

  • Gurler U (1997) Bivariate distribution and hazard function when a component is randomly truncated. J. Multivariate Anal. 60:20–47

    MathSciNet  Google Scholar 

  • Huang J, Vieland VJ, Wang K (2001) Nonparametric estimation of marginal distributions under bivariate truncation with application to testing for age-of-onset anticipation. Stat Sinica 11:1047–1068

    MathSciNet  Google Scholar 

  • Kalbfleisch JD, Lawless JF (1999) Regression models for right truncated data with applications to AIDS incubation times and reporting lags. Stat Sinica 1:19–32

    Google Scholar 

  • Lin DY, Sun W, Ying Z (1999) Nonparametric estimation of the gap time distributions for serial events with censored data. Biometrika 86:59–70

    Article  MathSciNet  Google Scholar 

  • Lin DY, Ying Z (1994) Semiparametric analysis of the additive risk model. Biometrika 81:61–71

    MathSciNet  Google Scholar 

  • Lin MR, Chang S-H, Pai L, Keyl PM (2003) A longitudinal study of risk factors for motorcycle crashes among junior colleges in Taiwan. Accid Anal Prev 35:243–252

    Article  Google Scholar 

  • Pruitt RC (1991) On negative mass assigned by the bivariate Kaplan–Meier estimation. Ann Statist 19:443–453

    MATH  MathSciNet  Google Scholar 

  • Tsai W-Y, Jewell NP, Wang M-C (1987) A note on the product-limit estimator under right censoring and left truncation. Biometrika 74:883–886

    Google Scholar 

  • Quale CM, van der Laan MJ (2000) Inference with bivariate truncated data. Lifetime Data Anal 6:391–408

    Article  MathSciNet  Google Scholar 

  • van der Laan MJ (1996) Nonparametric estimation of the bivariate survival function with truncated data. J Multivariate Anal 58:107–131

    MATH  MathSciNet  Google Scholar 

  • Visser M (1996) Nonparametric estimation of the bivariate survival function with an application to vertically transmitted AIDS. Biometrika 83:507–518

    Article  MATH  Google Scholar 

  • Wang M-C (1991) Nonparametric estimation from cross-sectional survival data. J Amer Stat Assoc 86:130–142

    MATH  Google Scholar 

  • Wang M-C (1999) Gap time bias in incident and prevalent cohorts. Stat Sinica 9:999–1010

    MATH  Google Scholar 

  • Wang W-J, Wells MT (1998) Nonparametric estimation of successive duration times under dependent censoring. Biometrika 85:561–572

    MathSciNet  Google Scholar 

Download references

Acknowledgments

We are very grateful to Associate Editor and two referees for their constructive comments that led to a significant improvement of this paper. We thank Dr. Mau-Roung Lin at Taipei Medical University Institute of Injury Prevention and Control for providing the anonymous motorcycle crash data. We also thank the Department of Health, Taiwan, R.O.C. for providing the Taiwan Cancer registry and Death registry data. Part of this work was supported by NSC-91-2118-M-002-003 from National Science Council in Taiwan.

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Correspondence to Shu-Hui Chang.

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Chang, SH., Tzeng, SJ. Nonparametric Estimation of Sojourn Time Distributions for Truncated Serial Event Data—a Weight-adjusted Approach. Lifetime Data Anal 12, 53–67 (2006). https://doi.org/10.1007/s10985-005-7220-9

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  • DOI: https://doi.org/10.1007/s10985-005-7220-9

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