Latent class instrumental variables: A clinical and biostatistical perspective

Stuart G. Baker, Barnett S. Kramer, Karen S. Lindeman

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


In some two-arm randomized trials, some participants receive the treatment assigned to the other arm as a result of technical problems, refusal of a treatment invitation, or a choice of treatment in an encouragement design. In some before-and-after studies, the availability of a new treatment changes from one time period to this next. Under assumptions that are often reasonable, the latent class instrumental variable (IV) method estimates the effect of treatment received in the aforementioned scenarios involving all-or-none compliance and all-or-none availability. Key aspects are four initial latent classes (sometimes called principal strata) based on treatment received if in each randomization group or time period, the exclusion restriction assumption (in which randomization group or time period is an instrumental variable), the monotonicity assumption (which drops an implausible latent class from the analysis), and the estimated effect of receiving treatment in one latent class (sometimes called efficacy, the local average treatment effect, or the complier average causal effect). Since its independent formulations in the biostatistics and econometrics literatures, the latent class IV method (which has no well-established name) has gained increasing popularity. We review the latent class IV method from a clinical and biostatistical perspective, focusing on underlying assumptions, methodological extensions, and applications in our fields of obstetrics and cancer research.

Original languageEnglish (US)
Pages (from-to)147-160
Number of pages14
JournalStatistics in Medicine
Issue number1
StatePublished - Jan 15 2016


  • All-or-none compliance
  • Causal inference
  • Encouragement design, observational
  • Paired availability design
  • Principal stratification, randomized trial

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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