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Title A Look on the Effects of Treatment Noncompliance in the Multivariate CACE Analysis via Bayesian Approach
Posted by Bernadette Tubo
Authors Abordo, Joseph P. and Tubo, Bernadette F.
Publication date 2022
Journal The Mindanawan Journal of Mathematics (TMJM)
Volume 4
Issue 1
Pages 13-22
Abstract The Complier Average Causal Effect (CACE) is a methodology that is popular in estimating the impact of an intervention among treatment even when there is noncompliance. Treatment noncompliance is a common issue in RCTs that may plague the randomization settings and may produce treatment effect estimates that are biased. Yue Ma in 2018 introduced the Multivariate CACE (MCACE) analysis and showed that the methodology out-performed the classical CACE methodology via the maximum likelihood estimation (MLE) approach [9]. This paper explores the behavior of the model treatment estimates for MCACE model via a Bayesian Estimation (BayesE) approach. This paper explores the behavior of the model treatment estimates of the MCACE model via a bayesian estimation (BayesE) approach. The proposed BayesE methodology explores impact on the treatment effect parameters when varied values of compliance rates p_c are imposed. Here, p_c∈ {20%,50%,80%} and a p_c of 20% implies an 80% noncompliance. The derived MCACE models are then compared to the derived MCACE models using MLE. Simulation study shows that as p_c increases from 20% to 80%, the derived treatment effect estimates of the MCACE model via BayesE gave more precise values than the treatment effect estimates derived via MLE. Comparison of the two models is based on its corresponding mean square error (MSE) values.
Index terms / Keywords noncompliance, multivariate, Complier Average Causal Effect, maximum likelihood estimation, Bayesian estimation