Skip to main content
SpringerLink
Log in

Analysis of Panel Count Data

  • Chapter
The Statistical Analysis of Interval-censored Failure Time Data

Part of the book series: Statistics for Biology and Health ((SBH))

  • 2932 Accesses

Abstract

As discussed in Section 1.3, doubly censored data occur in studies that consist of two related events with one followed by the other. A typical example is given by a disease progression study in which the onset of the disease is caused or preceded by certain virus infection. In these situations, three variables are present, and they are time to infection, time between infection and the onset of the disease, and time to the onset of the disease. It is apparent that one only needs to know two of the three variables. If the variable of interest is the time to infection or the time to the onset of the disease, in general, one only needs to analyze the variable of interest without the need of dealing with the other two variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

9.5 Bibliography, Discussion, and Remarks

  • Breslow, N. E. (1984). Extra-Poisson variation in log-linear models. Applied Statistics, 33, 38–44.

    Article  Google Scholar 

  • Chen, Y. Q., Wang, M-C. and Huang, Y. (2004). Semiparametric regression analysis on longitudinal pattern of recurrent gap times. Biostatistics, 5, 277–290.

    Article  MATH  Google Scholar 

  • Chen, B. E., Cook, R. J., Lawless, J. F. and Zhan, M. (2005). Statistical methods for multivariate interval-censored recurrent events. Statistics in Medicine, 24, 671–691.

    Article  MathSciNet  Google Scholar 

  • Cheng, S. C. and Wei, L. J. (2000). Inferences for a semiparametric model with panel data. Biometrika, 87, 89–97.

    Article  MATH  MathSciNet  Google Scholar 

  • Gaver, D. P. and O’Muircheartaigh, I. G. (1987). Robust empirical Bayes analyses of event rates. Technometrics, 29, 1–15.

    Article  MATH  MathSciNet  Google Scholar 

  • Hinde, J. (1982). Compound Poisson regression models. In GLIM 82: Proceedings of the International Conference in Generalized Linear Models, R. Gilchrist (ed), Berlin: Springer-Verlag, 109–121.

    Google Scholar 

  • Hu, X. J., Sun, J. and Wei, L. J. (2003). Regression parameter estimation from panel counts. Scandinavian Journal of Statistics, 30, 25–43.

    Article  MATH  MathSciNet  Google Scholar 

  • Huang, Y. and Chen, Y. Q. (2003). Marginal regression of gaps between recurrent events. Lifetime Data Analysis, 9, 293–303.

    Article  MATH  MathSciNet  Google Scholar 

  • Huang, C. Y. and Wang, M-C. (2004). Joint modeling and estimation for recurrent event processes and failure time data. Journal of the American Statistical Association, 99, 1153–1165.

    Article  MATH  MathSciNet  Google Scholar 

  • Ishwaran, H. and James, L. F. (2004). Computational methods for multiplicative intensity models using weighted gamma processes: proportional hazards, marked point processes, and panel count data. Journal of the American Statistical Association, 99, 175–190.

    Article  MATH  MathSciNet  Google Scholar 

  • Kalbfleisch, J. D. and Lawless, J. F. (1985). The analysis of panel data under a Markov assumption. Journal of the American Statistical Association, 80, 863–871.

    Article  MATH  MathSciNet  Google Scholar 

  • Lawless, J. F. and Zhan, M. (1998). Analysis of interval-grouped recurrentevent data using piecewise constant rate functions. The Canadian Journal of Statistics, 26, 549–565.

    Article  MATH  Google Scholar 

  • Lin, H. and Scharfstein, D. O. (2004). Analysis of longitudinal data with irregular outcome-dependent follow-up. Journal of Royal Statistical Society, Series B, 66, 791–813.

    Article  MATH  MathSciNet  Google Scholar 

  • Little, R. J. A. (1995). Modeling the drop-out mechanism in repeatedmeasures studies. Journal of the American Statistical Association, 90, 1112–1121.

    Article  MATH  MathSciNet  Google Scholar 

  • Robins, J. M., Rotnitzky, A. and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106–121.

    Article  MATH  MathSciNet  Google Scholar 

  • Sinha, D. and Maiti, T. (2004). A Bayesian approach for the analysis of panelcount data with dependent termination. Biometrics, 60, 34–40.

    Article  MATH  MathSciNet  Google Scholar 

  • Staniswalls, J. G., Thall, P. F. and Salch, J. (1997). Semiparametric regression analysis for recurrent event interval counts. Biometrics, 53, 1334–1353.

    Article  MathSciNet  Google Scholar 

  • Sun, J. and Fang, H. B. (2003). A nonparametric test for panel count data. Biometrika, 90, 199–208.

    Article  MATH  MathSciNet  Google Scholar 

  • Sun, J. and Kalbfleisch, J. D. (1993). The analysis of current status data on point processes. Journal of the American Statistical Association, 88, 1449–1454.

    Article  MATH  MathSciNet  Google Scholar 

  • Sun, J. and Kalbfleisch, J. D. (1995). Estimation of the mean function of point processes based on panel count data. Statistica Sinica, 5, 279–290.

    MATH  MathSciNet  Google Scholar 

  • Sun, J. and Rai, S. N. (2001). Nonparametric tests for the comparison of point processes based on incomplete data. Scandinavian Journal of Statistics, 28, 725–732.

    Article  MATH  MathSciNet  Google Scholar 

  • Sun, J. and Wei, L. J. (2000). Regression analysis of panel count data with covariate-dependent observation and censoring times. Journal of the Royal Statistical Society, Series B, 62, 293–302.

    Article  MathSciNet  Google Scholar 

  • Sun, L., Park, D. and Sun, J. (2006). The additive hazards model for recurrent gap times. Statistica Sinica, in press.

    Google Scholar 

  • Thall, P. F. (1988). Mixed Poisson likelihood regression models for longitudinal interval count data. Biometrics, 44, 197–209.

    Article  MATH  MathSciNet  Google Scholar 

  • Thall, P. F. (1989). Correction to: “Mixed Poisson likelihood regression models for longitudinal interval count data.” Biometrics, 45, 197–209.

    Article  Google Scholar 

  • Thall, P. F. and Lachin, J. M. (1988). Analysis of recurrent events: nonparametric methods for random-interval count data. Journal of the American Statistical Association, 83, 339–347.

    Article  Google Scholar 

  • Thall, P. F. and Vail, S. C. (1990) Some covariance models for longitudinal count data with overdispersion. Biometrics, 46, 657–671.

    Article  MATH  MathSciNet  Google Scholar 

  • Wang, Y. and Taylor, M. G. (2001). Jointly modeling longitudinal and event time data with application to Acquired Immunodeficiency Syndrome. Journal of the American Statistical Association, 96, 895–905.

    Article  MATH  MathSciNet  Google Scholar 

  • Wang, M-C., Qin, J., and Chiang, C-T. (2001). Analyzing recurrent event data with informative censoring. Journal of the American Statistical Association, 96, 1057–1065.

    Article  MATH  MathSciNet  Google Scholar 

  • Wellner, J. A. and Zhang, Y. (2000). Two estimators of the mean of a counting process with panel count data. Annual of Statistics, 28, 779–814.

    Article  MATH  MathSciNet  Google Scholar 

  • Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330–339.

    Article  MATH  MathSciNet  Google Scholar 

  • Zhang, Y. (2002). A semiparametric pseudolikelihood estimation method for panel count data. Biometrika, 89, 39–48.

    Article  MATH  MathSciNet  Google Scholar 

  • Zhang, Y. and Jamshidian, M. (2003). The gamma-frailty Poisson model for the nonparametric estimation of panel count data. Biometrics, 59, 1099–1106.

    Article  MATH  MathSciNet  Google Scholar 

  • Zhao, Q. and Sun, J. (2006). Semiparametric and nonparametric analysis of recurrent events with observation gaps. Computational Statistics and Data Analysis, in press.

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

(2006). Analysis of Panel Count Data. In: The Statistical Analysis of Interval-censored Failure Time Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-37119-2_9

Download citation

Publish with us

Policies and ethics

Search

Navigation