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Analysis of Chronic Disease Processes Based on Cohort and Registry Data

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Mathematical and Statistical Applications in Life Sciences and Engineering

Abstract

In this chapter, we review the types of observation schemes which arise in the analysis of data on chronic conditions from individuals in disease registries. We consider the utility of multistate modeling for such disease processes, and deal with both right-censored data and data arising from intermittent observation of individuals. The assumptions necessary to support standard likelihood or partial likelihood inference are highlighted and adaptations to deal with dependent censoring or dependent inspection are described and examined in simulation studies and through application.

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References

  1. Aalen, O.O., O. Borgan, and H. Fekjaer. 2001. Covariate adjustment of event histories estimated with Markov chains: The additive approach. Biometrics 57: 993–1001.

    Article  MathSciNet  MATH  Google Scholar 

  2. Andersen, P.K., O. Borgan, R.D. Gill, and N. Keiding. 1993. Statistical Models Based on Counting Processes. New York: Springer.

    Book  MATH  Google Scholar 

  3. Andersen, P.K., J.P. Klein, and S. Rosthoj. 2003. Generalised linear models for correlated pseudo-observations, with applications to multi-state models. Biometrika 90: 15–27.

    Article  MathSciNet  MATH  Google Scholar 

  4. Ayer, M., H.D. Brunk, G.M. Ewing, W.T. Reid, and E. Silverman. 1955. An empirical distribution function for sampling with incomplete observations. Annals of Mathematical Statistics 26: 641–647.

    Article  MathSciNet  MATH  Google Scholar 

  5. Barlow, R.E., D.J. Bartholomew, J.M. Bremner, and H.D. Brunk. 1972. Statistical inference under order restrictions: The theory and application of isotonic regression. New York: Wiley.

    MATH  Google Scholar 

  6. Cook, R.J., and J.F. Lawless. 2007. The statistical analysis of recurrent events. Berlin: Springer.

    MATH  Google Scholar 

  7. Cook, R.J., J.F. Lawless, L. Lakhal-Chaieb, and K.-A. Lee. 2009. Robust estimation of mean functions and treatment effects for recurrent events under event-dependent censoring and termination: application to skeletal complications in cancer metastatic to bone. Journal of the American Statistical Association 104: 60–75.

    Article  MathSciNet  MATH  Google Scholar 

  8. Datta, S., and G.A. Satten. 2001. Validity of the Aalen–Johansen estimators of stage occupation probabilities and Nelson–Aalen estimators of integrated transition hazards for non-markov models. Statistics and Probability Letters 55 (4): 403–411.

    Google Scholar 

  9. Datta, S., and G.A. Satten. 2002. Estimation of integrated transition hazards and stage occupation probabilities for non-markov systems under dependent censoring. Biometrics 58: 792–802.

    Article  MathSciNet  MATH  Google Scholar 

  10. Farewell, V.T., J.F. Lawless, D.D. Gladman, and M.B. Urowitz. 2003. Tracing studies and analysis of the effect of loss to follow-up on mortality estimation from patient registry data. Journal of the Royal Statistical Society Series C 52: 445–456.

    Article  MathSciNet  MATH  Google Scholar 

  11. Gill, R.D., Van der Laan, M.J. and Robins, J.M. 1997. Coarsening at random: characterizations, conjectures, counter-examples. In Processdings 1st Seattle Sypmposium in Biostatisitcs: Survival Analysis, eds. Lin D.Y and Fleming T.R, 256–294. New York: Springer.

    Google Scholar 

  12. Hogan, J.W., Roy, J. and Korkontzelou, C. 2004. Handling dropouts in longitudinal studies.

    Google Scholar 

  13. Hortobagyi, G.N., R.L. Theriault, A. Lipton, L. Porter, D. Blayney, C. Sinoff, H. Wheeler, J.F. Simeone, J.J. Seaman, R.D. Knight, M. Heffernan, K. Mellars, and D.J. Reitsma. 1998. Long-term prevention of skeletal complications of metastatic breast cancer with pamidronate. Protocol 19 Aredia Breast Cancer Study Group. Journal of Clinical Oncology 16: 2038–2044.

    Article  Google Scholar 

  14. Jackson, C.H. 2011. Multi-state models for panel data: The msm package for R. Journal Statistical Software 38: 1–28.

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  16. Kalbfleisch, J.D., and R.L. Prentice. 2002. The statistical analysis of failure time data (2nd ed.). New York: Wiley.

    Google Scholar 

  17. Klein, J.P., and M.L. Moeschberger. 2003. Survival analysis: Techniques for censored and truncated data, 2nd ed. New York: Springer.

    MATH  Google Scholar 

  18. Lawless, J.F. 2003. Statistical models and methods for lifetime data, 2nd ed. Hoboken: Wiley.

    MATH  Google Scholar 

  19. Lawless, J.F., and N. Nazeri Rad. 2015. Estimation and assessment of Markov multistate models with intermittent observations on individuals. Lifetime Data Analysis 21: 160–179.

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  21. Nelsen, R.B. 2006. An introduction to copulas, 2nd ed. New York: Springer.

    MATH  Google Scholar 

  22. Satten, G.A., S. Datta, and J. Robins. 2007. Estimating the marginal survival function in the presence of time dependent covariates. Statistics and Probability Letters 54: 397–403.

    Article  MathSciNet  MATH  Google Scholar 

  23. Scheike, T.H., and M.J. Zhang. 2007. Direct modelling of regression effects for transition probabilities in multistate models. Scandinavian Jorunal of Statistics 34: 17–32.

    Article  MathSciNet  Google Scholar 

  24. Van Der Laan, M.J., and J.M. Robins. 1998. Locally efficient estimation with current status data and time-dependent covariates. Journal of the American Statistical Association 93: 693–701.

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

    Richard J. Cook & Jerald F. Lawless

Authors
  1. Richard J. Cook
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  2. Jerald F. Lawless
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Correspondence to Richard J. Cook .

Editor information

Editors and Affiliations

  1. Department of Pure Mathematics, University of Calcutta, Kolkata, West Bengal, India

    Avishek Adhikari

  2. IMBIC, Kolkata, West Bengal, India

    Mahima Ranjan Adhikari

  3. Department of Mathematics and Statistics, Concordia University, Montreal, Québec, Canada

    Yogendra Prasad Chaubey

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Cook, R.J., Lawless, J.F. (2017). Analysis of Chronic Disease Processes Based on Cohort and Registry Data. In: Adhikari, A., Adhikari, M., Chaubey, Y. (eds) Mathematical and Statistical Applications in Life Sciences and Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-5370-2_15

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