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SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models

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  • Touraine, Célia
  • Gerds, Thomas A.
  • Joly, Pierre

Abstract

The irreversible illness-death model describes the pathway from an initial state to an absorbing state either directly or through an intermediate state. This model is frequently used in medical applications where the intermediate state represents illness and the absorbing state represents death. In many studies, disease onset times are not known exactly. This happens for example if the disease status of a patient can only be assessed at follow-up visits. In this situation the disease onset times are interval-censored. This article presents the SmoothHazard package for R. It implements algorithms for simultaneously fitting regression models to the three transition intensities of an illness-death model where the transition times to the intermediate state may be interval-censored and all the event times can be right-censored. The package parses the individual data structure of the subjects in a data set to find the individual contributions to the likelihood. The three baseline transition intensity functions are modelled by Weibull distributions or alternatively by M -splines in a semi-parametric approach. For a given set of covariates, the estimated transition intensities can be combined into predictions of cumulative event probabilities and life expectancies.

Suggested Citation

  • Touraine, Célia & Gerds, Thomas A. & Joly, Pierre, 2017. "SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i07).
  • Handle: RePEc:jss:jstsof:v:079:i07
    DOI: http://hdl.handle.net/10.18637/jss.v079.i07
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    References listed on IDEAS

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    1. Fay, Michael P. & Shaw, Pamela A., 2010. "Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i02).
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. Daniel Commenges & Pierre Joly & Anne Gégout‐Petit & Benoit Liquet, 2007. "Choice between Semi‐parametric Estimators of Markov and Non‐Markov Multi‐state Models from Coarsened Observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 33-52, March.
    4. Allignol, Arthur & Schumacher, Martin & Beyersmann, Jan, 2011. "Empirical Transition Matrix of Multi-State Models: The etm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i04).
    5. Micha Mandel, 2013. "Simulation-Based Confidence Intervals for Functions With Complicated Derivatives," The American Statistician, Taylor & Francis Journals, vol. 67(2), pages 76-81, May.
    6. Gandrud, Christopher, 2015. "simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i03).
    7. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).
    8. de Wreede, Liesbeth C. & Fiocco, Marta & Putter, Hein, 2011. "mstate: An R Package for the Analysis of Competing Risks and Multi-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i07).
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    1. Wanneveich, Mathilde & Jacqmin-Gadda, Hélène & Dartigues, Jean-François & Joly, Pierre, 2018. "Projections of health indicators for chronic disease under a semi-Markov assumption," Theoretical Population Biology, Elsevier, vol. 119(C), pages 83-90.
    2. Susanne Weber & Martin Wolkewitz & on behalf of COMBACTE‐MAGNET Consortium, 2020. "Accounting for length of hospital stay in regression models in clinical epidemiology," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 24-37, February.

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