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Accounting for missing data caused by drug cessation in observational comparative effectiveness research: a
Denis Mongin1, Kim Lauper2,3, Axel Finckh2,3
1Department of Medicine, University of Geneva, Geneva, Switzerland Denis.Mongin@unige.ch.
Annals of the Rheumatic Diseases
|January 14, 2022
Summary
Statistical methods for comparing drug effectiveness in observational studies with missing data were evaluated. CARRAC and multiple imputation accurately estimated comparative effectiveness, outperforming other methods sensitive to attrition.
Area of Science:
- Pharmacoepidemiology
- Biostatistics
- Health Outcomes Research
Background:
- Comparing drug effectiveness in observational studies is crucial for clinical decision-making.
- Attrition, or patient dropout, is a common challenge that can bias comparative effectiveness estimates.
- Existing statistical methods vary in their ability to handle missing data due to attrition.
Purpose of the Study:
- To evaluate the performance of various statistical methods in estimating comparative drug effectiveness when patient attrition is present.
- To identify methods robust to different patterns and amounts of missing data.
Main Methods:
- A simulation study was conducted using a realistic dataset to compare six methods: complete case analysis (CC), last observation carried forward (LOCF), LUNDEX, non-responder imputation (NRI), inverse probability weighting (IPW), and multiple imputation.
- Methods were assessed based on their ability to estimate low disease activity (LDA) at 1 year, with variations in attrition amount and missingness dependence.
- Specific methods incorporated treatment cessation reasons: multiple imputation (CARRAC) and IPW (IPW2, IPW1).
Main Results:
- LUNDEX and NRI significantly underestimated LDA differences and were highly sensitive to attrition.
- CC and IPW1 overestimated LDA differences, with overestimation increasing with attrition or missingness dependence.
- IPW2 and CARRAC provided unbiased estimates, though CARRAC demonstrated greater robustness to missing data patterns and attrition levels than IPW2.
Conclusions:
- Accurate comparative effectiveness estimates require methods that account for both confounding and reasons for treatment cessation.
- Multiple imputation (CARRAC) and IPW with cessation reasons (IPW2) are recommended for handling attrition in observational studies.
- Methods like LUNDEX, NRI, CC, and basic IPW are unreliable when significant attrition is present.
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