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Multistate models as a framework for estimand specification in clinical trials of complex processes
Alexandra Bühler1, Richard J Cook1, Jerald F Lawless1
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
Statistics in Medicine
|February 1, 2023
Summary
Intensity-based multistate models aid in analyzing complex clinical trial data, including interventions and patient dropouts. Careful definition of estimands is crucial for interpretable results in randomized trials with intercurrent events.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Epidemiology
Background:
- Multistate models are valuable for analyzing complex life history processes in randomized trials.
- Intercurrent events, such as interventions or loss to follow-up, complicate the interpretation of trial outcomes.
- Cumulative incidence function regression models are commonly used for estimating marginal process features.
Purpose of the Study:
- To discuss challenges in specifying estimands within intensity-based multistate models.
- To examine estimators for marginal process features in the presence of intercurrent events.
- To emphasize the importance of interpretable estimands in clinical trial design and analysis.
Main Methods:
- Utilizing intensity-based multistate models.
- Analyzing cumulative incidence function regression models.
- Investigating the specification and interpretation of estimands.
Main Results:
- Identified issues in estimand specification for multistate models.
- Demonstrated limiting values of common estimators for marginal process features.
- Highlighted the need for carefully defined, interpretable target estimands when intercurrent events occur.
Conclusions:
- Intensity-based multistate models offer a robust framework for clinical trial analysis.
- Clear definition of estimands is essential for valid inference, especially with intercurrent events.
- Trial protocols should guide the interpretation of estimands based on marginal features.
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