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Analyzing longitudinal data with informative observation times under biased sampling

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  • Sun, Liuquan
  • Tong, Xingwei

Abstract

In many situations, longitudinal responses may be correlated with observation times as well as censoring time. This paper considers the regression analysis of longitudinal data where these correlations may exist under biased sampling, and a joint modeling approach that uses some latent variables to characterize the correlations is proposed. For inference about regression parameters, estimating equation approaches are developed and asymptotic properties of the proposed estimators are established. The finite sample behavior of the methods is examined through simulation studies, and an application to a data set from a bladder cancer study is provided for illustration.

Suggested Citation

  • Sun, Liuquan & Tong, Xingwei, 2009. "Analyzing longitudinal data with informative observation times under biased sampling," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1162-1168, May.
  • Handle: RePEc:eee:stapro:v:79:y:2009:i:9:p:1162-1168
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    References listed on IDEAS

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    1. Sun, Jianguo & Sun, Liuquan & Liu, Dandan, 2007. "Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1397-1406, December.
    2. Chiung-Yu Huang & Mei-Cheng Wang, 2004. "Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1153-1165, December.
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    5. Sun, Jianguo & Park, Do-Hwan & Sun, Liuquan & Zhao, Xingqiu, 2005. "Semiparametric Regression Analysis of Longitudinal Data With Informative Observation Times," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 882-889, September.
    6. Torben Martinussen & Thomas H. Scheike, 2001. "Sampling Adjusted Analysis of Dynamic Additive Regression Models for Longitudinal Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 303-323, June.
    7. J. Sun & L. J. Wei, 2000. "Regression analysis of panel count data with covariate‐dependent observation and censoring times," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 293-302.
    8. Ying Zhang, 2002. "A semiparametric pseudolikelihood estimation method for panel count data," Biometrika, Biometrika Trust, vol. 89(1), pages 39-48, March.
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    10. Lin D Y & Ying Z, 2001. "Semiparametric and Nonparametric Regression Analysis of Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 103-126, March.
    11. Welsh A.H. & Lin X. & Carroll R.J., 2002. "Marginal Longitudinal Nonparametric Regression: Locality and Efficiency of Spline and Kernel Methods," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 482-493, June.
    12. Wang Y. & Taylor J. M. G., 2001. "Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 895-905, September.
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