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Controlling for Differential Regression-To-The-Mean via Propensity Scores: A Simulation Study
Chase D Latour1,2, Leah J McGrath2, Mary Clouser3
1Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Clinical Epidemiology
|June 7, 2023
Summary
Regression-to-the-mean (RTM) bias in observational studies can be reduced using propensity scores. Adjusting for historical lab values, especially the mean of prior counts, effectively mitigates RTM bias in comparative effectiveness research.
Area of Science:
- Epidemiology
- Biostatistics
- Health Services Research
Background:
- Regression-to-the-mean (RTM) is a statistical phenomenon potentially biasing observational studies.
- Inclusion based on extreme measurements can introduce differential RTM across treatment groups.
- This bias poses challenges for comparative effectiveness and safety studies.
Purpose of the Study:
- To investigate propensity score methods for mitigating RTM bias.
- To evaluate the effectiveness of these methods via simulation.
- To address bias in observational studies indexing patients on extreme values.
Main Methods:
- Simulated a comparative effectiveness study for immune thrombocytopenia (ITP).
- Generated platelet counts based on ITP severity, a confounder.
- Used propensity scores and inverse probability of treatment weights to adjust for RTM.
Main Results:
- Propensity score adjustment significantly reduced bias and increased precision.
- Adjusting for combinations of summary metrics was most effective.
- Mean of prior platelet counts or difference from qualifying count showed greatest individual bias reduction.
Conclusions:
- Propensity score models incorporating historical lab value summaries can address differential RTM.
- This approach is applicable to comparative effectiveness and safety studies.
- Careful selection of summary metrics is crucial for optimal bias reduction.
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