# Local average treatment effects estimation via substantive model compatible multiple imputation

## TL;DR

This paper introduces a novel application of substantive model compatible multiple imputation (SMC MIC) to estimate local average treatment effects in randomized controlled trials, especially for binary outcomes, improving upon existing methods.

## Contribution

It proposes using SMC MIC to impute latent compliance classes in mixture models, enhancing the estimation of causal effects in RCTs with non-adherence.

## Key findings

- SMC MIC outperforms two-stage methods for binary outcomes.
- SMC MIC is more efficient than Bayesian methods with auxiliary variables.
- The approach improves estimation accuracy in simulated and real RCT data.

## Abstract

Non-adherence to assigned treatment is common in randomised controlled trials (RCTs). Recently, there has been an increased interest in estimating causal effects of treatment received, for example the so-called local average treatment effect (LATE). Instrumental variables (IV) methods can be used for identification, with estimation proceeding either via fully parametric mixture models or two-stage least squares (TSLS). TSLS is popular but can be problematic for binary outcomes where the estimand of interest is a causal odds ratio. Mixture models are rarely used in practice, perhaps because of their perceived complexity and need for specialist software. Here, we propose using multiple imputation (MI) to impute the latent compliance class appearing in the mixture models. Since such models include an interaction term between compliance class and randomised treatment, we use `substantive model compatible' MI (SMC MIC), which can also address other missing data, before fitting the mixture models via maximum likelihood to the MI datasets and combining results via Rubin's rules. We use simulations to compare the performance of SMC MIC to existing approaches and also illustrate the methods by re-analysing a RCT in UK primary health. We show that SMC MIC can be more efficient than full Bayesian estimation when auxiliary variables are incorporated, and is superior to two-stage methods, especially for binary outcomes.