Instrumental variable approaches for estimating complier average causal effects on bivariate outcomes in randomised trials with non-compliance

TL;DR

This paper extends instrumental variable methods to estimate causal effects on bivariate outcomes in RCTs with non-compliance, emphasizing the importance of modeling outcome correlation for accurate inference.

Contribution

It introduces a Bayesian full likelihood approach and a three-stage least squares method that jointly model outcomes and treatment effects, accounting for their correlation.

Findings

BFL and 3sls provide unbiased estimates with good coverage.

Ignoring outcome correlation leads to biased estimates and poor confidence interval coverage.

Simulation results demonstrate the effectiveness of the proposed methods.

Abstract

In Randomised Controlled Trials (RCT) with treatment non-compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to cost-effectiveness analyses, where methods need to recognise the correlation between cost and health outcomes. We propose a Bayesian full likelihood (BFL) approach, which jointly models the effects of random assignment on treatment received and the outcomes, and a three-stage least squares (3sls) method, which acknowledges the correlation between the endpoints, and the endogeneity of the treatment received. This investigation is motivated by the REFLUX study, which exemplifies the setting where compliance differs between the RCT and routine practice. A simulation is used to compare the methods performance. We find that failure to model the correlation between the outcomes and treatment received correctly can result in poor CI coverage and biased estimates. By contrast, BFL and 3sls methods provide unbiased estimates with good coverage.