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msSurv: An R Package for Nonparametric Estimation of Multistate Models

Author

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  • Ferguson, Nicole
  • Datta, Somnath
  • Brock, Guy

Abstract

We present an R package, msSurv, to calculate the marginal (that is, not conditional on any covariates) state occupation probabilities, the state entry and exit time distributions, and the marginal integrated transition hazard for a general, possibly non-Markov, multistate system under left-truncation and right censoring. For a Markov model, msSurv also calculates and returns the transition probability matrix between any two states. Dependent censoring is handled via modeling the censoring hazard through observable covariates. Pointwise confidence intervals for the above mentioned quantities are obtained and returned for independent censoring from closed-form variance estimators and for dependent censoring using the bootstrap.

Suggested Citation

  • Ferguson, Nicole & Datta, Somnath & Brock, Guy, 2012. "msSurv: An R Package for Nonparametric Estimation of Multistate Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i14).
    • Handle: RePEc:jss:jstsof:v:050:i14
    DOI: http://hdl.handle.net/10.18637/jss.v050.i14
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    References listed on IDEAS

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    1. Somnath Datta & Glen A. Satten, 2002. "Estimation of Integrated Transition Hazards and Stage Occupation Probabilities for Non-Markov Systems Under Dependent Censoring," Biometrics, The International Biometric Society, vol. 58(4), pages 792-802, December.
    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. Somnath Datta & Rajeshwari Sundaram, 2006. "Nonparametric Estimation of Stage Occupation Probabilities in a Multistage Model with Current Status Data," Biometrics, The International Biometric Society, vol. 62(3), pages 829-837, September.
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    2. Frans Willekens & Hein Putter, 2014. "Software for multistate analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(14), pages 381-420.
    3. Guglielmo D'Amico & Stefania Scocchera & Loriano Storchi, 2021. "Randentropy: a software to measure inequality in random systems," Papers 2103.09107, arXiv.org.

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