Estimating the Complier Average Causal Effect via a latent class approach using gsem

Patricio Troncoso, Ana Morales-Gómez

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Abstract

In randomized control trials (RCT), intention-to-treat (ITT) analysis is customarily used to estimate the effect of the trial; however, in the presence of noncompliance, this can often lead to biased estimates because ITT completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect (CACE) approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the CACE estimate can be obtained via a latent class specification using the gsem command.
Original languageEnglish
Pages (from-to)404-415
Number of pages12
JournalStata Journal
Volume22
Issue number2
DOIs
Publication statusPublished - 30 Jun 2022

Keywords

  • gsem
  • Complier Average Causal Effect (CACE)
  • Randomized Control Trial (RCT)
  • Compliance
  • Adherence
  • Latent class modeling
  • Mixture modeling

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