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Methods for estimating complier average causal effects for cost‐effectiveness analysis
Author(s) -
DiazOrdaz K.,
Franchini A. J.,
Grieve R.
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12294
Subject(s) - instrumental variable , endogeneity , econometrics , statistics , bayesian probability , average treatment effect , confidence interval , randomized controlled trial , randomized experiment , contrast (vision) , point estimation , mathematics , computer science , medicine , propensity score matching , artificial intelligence , surgery
Summary In randomized controlled trials 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 recognize the correlation between cost and health outcomes. We propose a Bayesian full likelihood approach, which jointly models the effects of random assignment on treatment received and the outcomes, and a three‐stage least squares method, which acknowledges the correlation between the end points and the endogeneity of the treatment received. This investigation is motivated by the REFLUX study, which exemplifies the setting where compliance differs between the randomized controlled trial 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 confidence interval coverage and biased estimates. By contrast, Bayesian full likelihood and three‐stage least squares methods provide unbiased estimates with good coverage.