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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

    as
    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. Haiqun Lin & Daniel O. Scharfstein & Robert A. Rosenheck, 2004. "Analysis of longitudinal data with irregular, outcome‐dependent follow‐up," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 791-813, August.
    3. 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.
    4. Jianqing Fan & Runze Li, 2004. "New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 710-723, January.
    5. 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.
    6. Ying Zhang, 2002. "A semiparametric pseudolikelihood estimation method for panel count data," Biometrika, Biometrika Trust, vol. 89(1), pages 39-48, March.
    7. Wang M-C. & Qin J. & Chiang C-T., 2001. "Analyzing Recurrent Event Data With Informative Censoring," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1057-1065, September.
    8. 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.
    9. 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.
    10. Roy J. & Lin X., 2002. "Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates: Changes in Methadone Treatment Practices," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 40-52, March.
    11. 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.
    12. 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.
    13. 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.
    14. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Richard Gelber & Steven Lipshultz, 2002. "Parameter Estimation in Longitudinal Studies with Outcome-Dependent Follow-Up," Biometrics, The International Biometric Society, vol. 58(3), pages 621-630, September.
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