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Methods for population adjustment with limited access to individual patient data: A review and simulation study
Antonio Remiro-Azócar1,2, Anna Heath1,3,4, Gianluca Baio1
1Department of Statistical Science, University College London, London, UK.
Research Synthesis Methods
|July 1, 2021
Summary
Matching-adjusted indirect comparison (MAIC) offers the most accurate treatment effect estimates in population-adjusted indirect comparisons. Standard indirect comparisons and simulated treatment comparison (STC) methods show significant bias, especially with covariate imbalance.
Area of Science:
- Health Services Research
- Biostatistics
- Epidemiology
Background:
- Population-adjusted indirect comparisons are crucial for estimating treatment effects when individual patient data is unavailable.
- Methods like matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC) are popular but lack comprehensive evaluation.
- Cross-trial differences in effect modifiers necessitate robust comparison methods.
Purpose of the Study:
- To formally evaluate and compare the accuracy of standard unadjusted indirect comparisons, MAIC, and STC.
- To assess the performance of these methods across various scenarios, particularly for survival outcomes.
- To identify biases and limitations inherent in each indirect comparison technique.
Main Methods:
- A comprehensive simulation study was conducted across 162 scenarios.
- Survival outcomes and continuous covariates were assumed, with the log hazard ratio as the effect measure.
- Standard indirect comparisons, MAIC, and STC were systematically analyzed.
Main Results:
- MAIC provided unbiased treatment effect estimates when assumptions were met.
- Standard indirect comparisons exhibited systematic bias, exacerbated by covariate imbalance and interaction effects.
- Simulated treatment comparison (STC) typically produced biased results due to targeting conditional rather than marginal treatment effects.
Conclusions:
- MAIC generally offers the most accurate estimates among the evaluated methods.
- Standard indirect comparisons and STC demonstrate significant bias and coverage issues.
- Further development of STC is needed to target marginal treatment effects for improved accuracy.
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