Re: "Dealing with missing outcome data in randomized trials and observational studies"

American Journal of Epidemiology

Volumn 176, Issue 4, 2012, Pages 357-358

  Liublinska, Victoria ^a   Rubin, Donald B ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYES THEOREM; HUMAN; LETTER; MULTIVARIATE ANALYSIS; OBSERVATIONAL STUDY; RANDOMIZED CONTROLLED TRIAL (TOPIC); SENSITIVITY ANALYSIS; SIMULATION;

HUMANS; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 84864960628     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kws215     Document Type: Letter

References (7)

1. Groenwold RH, Donders ART, Roes KCB, et al. Dealing with missing outcome data in randomized trials and observational studies. Am J Epidemiol. 2012;175(3): 210-217.
2. Rubin DB. Inference and missing data. Biometrika. 1976; 63(3):581-592.
3. van Buuren S, Groothuis-Oudshoorn K. MICE: Multivariate Imputation by Chained Equations. R Package, Version 1.21. Leiden, the Netherlands: Stef van Buuren; 2009.
4. Rubin DB. Multiple Imputation for Non Response in Surveys. New York, NY: John Wiley & Sons, Inc; 1987.
5. Little RJA, Rubin DB. Statistical Analysis With Missing Data. 2nd ed. New York, NY: John Wiley & Sons, Inc.; 2002.
6. Rubin DB. Characterizing the estimation of parameters in incomplete data problems. J Am Stat Assoc. 1974;69(346): 467-474.
7. National Research Council. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press; 2010. (http://www.nap.edu/openbook.php?record-id=12955). (Accessed April 17, 2012).