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Estimating cluster-level local average treatment effects in cluster randomised trials with non-adherence
Schadrac C Agbla1, Bianca De Stavola2, Karla DiazOrdaz1
1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, UK.
Statistical Methods in Medical Research
|May 25, 2019
Summary
Non-adherence in cluster randomized trials complicates treatment effect estimation. This study demonstrates that two-stage least squares regression using cluster summaries can estimate the local average treatment effect, improving efficiency with baseline variable adjustments.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Epidemiology
Background:
- Non-adherence to assigned treatment is a prevalent challenge in cluster randomized trials (CRTs).
- Estimating treatment efficacy, specifically the local average treatment effect (LATE), is often of interest in CRTs with non-adherence.
- Instrumental variable methods are increasingly advocated for LATE estimation, but their application in CRTs requires careful consideration of clustered randomization.
Purpose of the Study:
- To demonstrate the feasibility of estimating the local average treatment effect (LATE) in cluster randomized trials (CRTs) with non-adherence using two-stage least squares (2SLS) regression with cluster-level summaries.
- To propose and evaluate methods for improving the efficiency and validity of LATE estimation in CRTs, including the use of baseline variables for adjustment.
- To assess the performance of the proposed 2SLS method through simulations and illustrate its application in a real-world CRT.
Main Methods:
- Utilized two-stage least squares (2SLS) regression analysis on cluster-level summaries of outcomes and treatment received.
- Proposed adjusting cluster-level summaries using baseline variables to enhance the efficiency of 2SLS estimation.
- Conducted simulation studies to evaluate the performance of 2SLS under various non-adherence scenarios (cluster-level and individual-level), incorporating weighting and robust standard errors.
- Investigated the impact of baseline covariate adjustment and appropriate degrees of freedom correction for valid inference.
Main Results:
- Two-stage least squares (2SLS) estimation using cluster-level summaries yielded estimates with minimal bias and coverage probabilities close to the nominal level.
- The use of baseline variables for adjusting cluster-level summaries improved the efficiency of the 2SLS estimation.
- Valid inferences were achieved when accounting for reduced sample size, potential heteroscedasticity, and employing appropriate small sample degrees of freedom correction and robust standard errors.
Conclusions:
- Two-stage least squares (2SLS) regression with cluster-level summaries offers a viable approach for estimating the local average treatment effect (LATE) in cluster randomized trials (CRTs) with non-adherence.
- Adjusting cluster-level summaries with baseline variables enhances the efficiency of LATE estimation.
- Accurate inference requires careful implementation, including appropriate small sample degrees of freedom correction and robust standard errors, particularly in the context of cluster-level non-adherence.
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