Handling Missing Data in Randomized Experiments with Noncompliance

Booil Jo, Elizabeth M. Ginexi, Nicholas S. Ialongo

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Treatment noncompliance and missing outcomes at posttreatment assessments are common problems in field experiments in naturalistic settings. Although the two complications often occur simultaneously, statistical methods that address both complications have not been routinely considered in data analysis practice in the prevention research field. This paper shows that identification and estimation of causal treatment effects considering both noncompliance and missing outcomes can be relatively easily conducted under various missing data assumptions. We review a few assumptions on missing data in the presence of noncompliance, including the latent ignorability proposed by Frangakis and Rubin (Biometrika 86:365-379, 1999), and show how these assumptions can be used in the parametric complier average causal effect (CACE) estimation framework. As an easy way of sensitivity analysis, we propose the use of alternative missing data assumptions, which will provide a range of causal effect estimates. In this way, we are less likely to settle with a possibly biased causal effect estimate based on a single assumption. We demonstrate how alternative missing data assumptions affect identification of causal effects, focusing on the CACE. The data from the Johns Hopkins School Intervention Study (Ialongo et al., Am J Community Psychol 27:599-642, 1999) will be used as an example.

Original languageEnglish (US)
Pages (from-to)384-396
Number of pages13
JournalPrevention Science
Issue number4
StatePublished - Dec 2010


  • Causal inference
  • Complier average causal effect
  • Latent ignorability
  • Missing at random
  • Missing data
  • Noncompliance

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


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