Handling Missing Data in Randomized Experiments with Noncompliance
- 324 Downloads
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.
KeywordsCausal inference Complier average causal effect Latent ignorability Missing at random Missing data Noncompliance
- Emsley, R., Dunn, G., & White, I. R. (2010). Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research. doi: 10.1177/0962280209105014.
- Ialongo, N. S., Werthamer, L., Kellam, S. G., Brown, C. H., Wang, S., & Lin, Y. (1999). Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression and antisocial behavior. American Journal of Community Psychology, 27, 599–642.CrossRefPubMedGoogle Scholar
- Jo, B., & Vinokur, A. (2010). Sensitivity analysis and bounding of causal effects with alternative identifying assumptions. Journal of Educational and Behavioral Statistics, in press.Google Scholar
- Kellam, S. G., Branch, J. D., Agrawal, K. C., & Ensminger, M. E. (1975). Mental health and going to school: The Woodlawn program of assessment, early intervention, and evaluation. Chicago: University of Chicago Press.Google Scholar
- Little, R. J. A. & Rubin, D. B. (2002). Statistical analysis with missing data. New York: Wiley.Google Scholar
- Muthén, L. K., & Muthén, B. O. (1998–2009). Mplus user’s guide. Los Angeles: Muthén & Muthén.Google Scholar
- Neyman, J. (1923). On the application of probability theory to agricultural experiments. Section 9 translated in Statistical Science, 5, 465–480 (1990).Google Scholar
- Rubin, D. B. (1990). Comment on “Neyman (1923) and causal inference in experiments and observational studies.” Statistical Science, 5, 472–480.Google Scholar