期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 30, 期 5, 页码 1373-1392出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280221997507
关键词
Bayesian estimation; frequentist (maximum likelihood) estimation; Kolmogorov-Chapman forward equations; multistate models; partially observed aggregated data; WinBUGS
类别
资金
- Developing Excellence in Leadership, Training and Science (DELTAS) Africa Initiative Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) [DEL-15-005]
- New Partnership for Africa's Development Planning and Coordinating Agency (NEPAD Agency)
- Wellcome Trust [107754/Z/15/Z]
- UK government
- Wits University Research Committee (URC) [URC-2021]
Multistate models are widely used in fields such as biomedical research and health economics to describe the time-to-event life history of an individual. Estimation quantities in these models include transition probabilities and rates, which can be mapped using Bayesian estimation and Kolmogorov-Chapman forward equations. While most models assume Markov property and time homogeneity, extensions to non-Markovian and time-varying transition rates are possible.
There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.
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