Estimating the complier average causal effect via a latent class approach using gsem

STATA JOURNAL(2022)

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摘要
In randomized controlled trials, intention-to-treat 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 intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect 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 complier average causal effect estimate can be obtained via a latent class specification using the gsem command.
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关键词
st0677,gsem,complier average causal effect,randomized control trial,compliance,adherence,latent class modeling,mixture modeling
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