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The Utility of Multistate Models: A Flexible Framework for Time-to-Event Data
Jennifer G Le-Rademacher1, Terry M Therneau1, Fang-Shu Ou1
1Division of Clinical Trials and Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA.
Current Epidemiology Reports
|August 25, 2022
Summary
Multistate models offer a flexible framework for analyzing complex diseases, extending beyond traditional survival analyses like Kaplan-Meier curves and Cox models. This review introduces their terminology and applications for estimating absolute and relative risks in medical research.
Area of Science:
- Medical Statistics
- Biostatistics
- Epidemiology
Background:
- Survival analyses are crucial in medical research, with Kaplan-Meier curves and Cox models being widely recognized.
- Multistate models, though introduced in 1965, are gaining recent traction in the medical research community.
- Familiarity with multistate models remains limited despite their potential.
Purpose of the Study:
- To introduce common terminologies and estimable quantities from multistate models.
- To illustrate the utility of multistate models using examples from published literature.
- To encourage the adoption of multistate models for time-to-event data analysis.
Main Methods:
- Depiction of models using states and transitions.
- Estimation of clinically meaningful quantities such as probability in a state, time in a state, and number of visits.
- Utilizing multistate hazard models for relative risk estimation.
Main Results:
- Multistate models allow simultaneous analysis of multiple disease pathways.
- They provide insights into the natural history of complex diseases.
- Absolute risks (probability, time, visits) and relative risks can be estimated.
Conclusions:
- Multistate models offer a more general and flexible framework than Kaplan-Meier or Cox models.
- They are valuable for understanding complex diseases and time-to-event data.
- Their application is strongly encouraged in medical research for comprehensive survival analysis.
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