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Flexible parametric illness-death models

Author

Listed:
  • Sally R. Hinchliffe

    (Department of Health Sciences, University of Leicester)

  • David A. Scott

    (Oxford Outcomes Ltd)

  • Paul C. Lambert

    (Department of Health Sciences, University of Leicester)

Abstract

It is usual in time-to-event data to have more than one event of interest, for example, time to death from different causes. Competing risks models can be applied in these situations where events are considered mutually exclusive absorbing states. That is, we have some initial state—for example, alive with a diagnosis of cancer—and we are interested in several different endpoints, all of which are final. However, the progression of disease will usually consist of one or more intermediary events that may alter the progression to an endpoint. These events are neither initial states nor absorbing states. Here we consider one of the simplest multistate models, the illness-death model. stpm2illd is a postestimation command used after fitting a flexible parametric survival model with stpm2 to estimate the probability of being in each of four states as a function of time. There is also the option to generate confidence intervals and transition hazard functions. The new command is illustrated through a simple example. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Sally R. Hinchliffe & David A. Scott & Paul C. Lambert, 2013. "Flexible parametric illness-death models," Stata Journal, StataCorp LP, vol. 13(4), pages 759-775, December.
    • Handle: RePEc:tsj:stataj:v:13:y:2013:i:4:p:759-775
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    Cited by:

    1. Lauren Scott & Chris Rogers, 2016. "Creating summary tables using the sumtable command," United Kingdom Stata Users' Group Meetings 2016 05, Stata Users Group.
    2. Michael Crowther & Paul Lambert, 2016. "Multistate survival analysis in Stata," United Kingdom Stata Users' Group Meetings 2016 02, Stata Users Group.
    3. Xudong Du & Mier Li & Ping Zhu & Ju Wang & Lisha Hou & Jijie Li & Hongdao Meng & Muke Zhou & Cairong Zhu, 2018. "Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.

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