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Methods for estimating complier average causal effects for cost-effectiveness analysis.

K DiazOrdaz1, A J Franchini1, R Grieve1

  • 1London School of Hygiene and Tropical Medicine UK.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|January 23, 2018
PubMed
Summary
This summary is machine-generated.

Instrumental variable methods for causal inference in randomized trials are extended for cost-effectiveness analysis. New Bayesian and three-stage least squares approaches provide unbiased estimates and good coverage, even with treatment non-compliance.

Keywords:
Bivariate outcomesCost‐effectivenessInstrumental variablesNon‐compliance

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Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial methodology

Background:

  • Randomized controlled trials (RCTs) often face treatment non-compliance.
  • Estimating causal effects in RCTs with non-compliance typically uses instrumental variable (IV) methods.
  • Extending IV methods to cost-effectiveness analysis (CEA) requires accounting for the correlation between costs and health outcomes.

Purpose of the Study:

  • To extend instrumental variable approaches for causal inference to cost-effectiveness analyses.
  • To propose and evaluate novel statistical methods that handle treatment non-compliance and correlated outcomes in CEA.
  • To address the endogeneity of treatment received in the presence of non-compliance.

Main Methods:

  • Proposed a Bayesian full likelihood approach to jointly model treatment assignment, received treatment, and outcomes.
  • Introduced a three-stage least squares (3SLS) method to account for outcome correlations and treatment endogeneity.
  • Utilized simulation studies to compare the performance of the proposed methods against existing approaches.

Main Results:

  • Failure to correctly model the correlation between outcomes and received treatment leads to biased estimates and poor confidence interval coverage.
  • The proposed Bayesian full likelihood and 3SLS methods yield unbiased estimates.
  • Both proposed methods demonstrate good confidence interval coverage in simulations.

Conclusions:

  • Accurate modeling of treatment non-compliance and its impact on costs and outcomes is crucial in CEA.
  • Bayesian full likelihood and 3SLS methods are effective for handling endogeneity and correlated outcomes in RCT-based CEA.
  • These advanced methods improve the reliability of causal effect estimates in cost-effectiveness studies with non-compliance.