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Recent developments in discrete-time multistate estimation in Stata

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  • Daniel C. Schneider

    (Max Planck Institute for Demographic Research)

Abstract

Multistate life tables (MSLTs), or multistate survival models, have become a widely used analytical framework in the social and health sciences. These models can be cast in continuous or discrete time. The dtms Stata module (dtms stands for "discretetime multistate"), which was presented at the German Stata Conference 2023 (Schneider 2023), implements the estimation of the discrete-time flavor of these models. This presentation first outlines discrete-time multistate estimation and then gives an overview of recent package enhancements including the following: external multinomial logistic estimation results, for example, from the interpolated Markov chain (IMaCh) executable (Brouard 2021), can be imported for further processing; difficulties with reloading saved dtms files across package versions have been resolved; the initial state distribution has been incorporated into the asymptotic analysis; new result type “evol” calculates the evolution of population fractions, along with the corresponding covariance matrix; estimation based on restricted transitions has been improved; transition probabilities can be based on time-varying covariate values; and several dtms trees can now be held in memory.

Suggested Citation

  • Daniel C. Schneider, 2025. "Recent developments in discrete-time multistate estimation in Stata," German Stata Conference 2025 08, Stata Users Group.
    • Handle: RePEc:boc:dsug25:08
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    File URL: http://repec.org/dsug2025/Germany25_Schneider.pdf
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    References listed on IDEAS

    as
    1. Daniel C. Schneider & Mikko Myrskylä, 2023. "Statistical inference for discrete-time multistate models: extensions to Markov Chains with rewards," MPIDR Working Papers WP-2023-042, Max Planck Institute for Demographic Research, Rostock, Germany.
    2. Christian Dudel & Mikko Myrskylä, 2017. "Working Life Expectancy at Age 50 in the United States and the Impact of the Great Recession," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2101-2123, December.
    3. Daniel C. Schneider, 2023. "Statistical inference for discrete-time multistate models: asymptotic covariance matrices, partial age ranges, and group contrasts," MPIDR Working Papers WP-2023-041, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. Hal Caswell & Silke van Daalen, 2021. "Healthy longevity from incidence-based models: More kinds of health than stars in the sky," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(13), pages 397-452.
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