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A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials
Wei Wang1, Guangyu Tong2,3,4, Shashivadan P Hirani5
1Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Statistics in Medicine
|November 21, 2023
Summary
Estimating treatment effects with missing quality of life data is challenging. This study introduces a new statistical method for cluster-randomized trials to address informative missingness, improving causal effect estimation.
Area of Science:
- Biostatistics
- Epidemiology
- Health Services Research
Background:
- Informative censoring of quality of life (QOL) outcomes in medical studies presents a significant statistical challenge.
- Existing methods for handling missing QOL data, such as composite outcomes or imputation, rely on untestable assumptions.
- The survivor average causal effect (SACE) offers an alternative estimand, but methods for its estimation in cluster-randomized trials are limited.
Purpose of the Study:
- To develop and evaluate a statistical methodology for estimating the SACE in cluster-randomized trials.
- To address the gap in methods for handling informative missingness in QOL outcomes within clustered study designs.
- To provide a robust approach for causal inference when outcomes are subject to informative dropout.
Main Methods:
- A mixed-effects model approach is proposed to estimate the SACE, accounting for intracluster correlations.
- An expectation-maximization algorithm is utilized for parameter estimation within the mixed-effects framework.
- The proposed method models principal strata membership, with and without random intercepts, to capture complex data structures.
Main Results:
- Simulations demonstrate the performance of the proposed mixed-effects approach compared to a fixed-effects method.
- The results highlight the importance of accounting for intracluster correlation in cluster-randomized trials for accurate SACE estimation.
- The methodology is validated using a real-world cluster-randomized trial assessing telecare interventions.
Conclusions:
- The developed mixed-effects model provides a statistically sound method for estimating SACE in cluster-randomized trials with informative missing data.
- This approach offers an improvement over existing methods by appropriately handling clustered data structures and informative censoring.
- The findings have implications for the design and analysis of clinical trials evaluating interventions, particularly those measuring health-related QOL.
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