This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Barnard, J. Articles by Meng, X.-L. Search for Related Content PubMed PubMed Citation Articles by Barnard, J. Articles by Meng, X.-L. Social Bookmarking                         What's this?

Applications of multiple imputation in medical studies: from AIDS to NHANES

Department of Statistics, Harvard University, Massachusetts, USA

Xiao-Li Meng

Department of Statistics, The University of Chicago, Illinois, USA, meng{at}galton.uchicago.edu

Rubin's multiple imputation is a three-step method for handling complex missing data, or more generally, incomplete-data problems, which arise frequently in medical studies. At the first step, m (> 1) completed-data sets are created by imputing the unobserved data m times using m independent draws from an imputation model, which is constructed to reasonably approximate the true distributional relationship between the unobserved data and the available information, and thus reduce potentially very serious nonresponse bias due to systematic difference between the observed data and the unobserved ones. At the second step, m complete-data analyses are performed by treating each completed-data set as a real complete-data set, and thus standard complete-data procedures and software can be utilized directly. At the third step, the results from the m complete-data analyses are combined in a simple, appropriate way to obtain the so-called repeated-imputation inference, which properly takes into account the uncertainty in the imputed values. This paper reviews three applications of Rubin's method that are directly relevant for medical studies. The first is about estimating the reporting delay in acquired immune deficiency syndrome (AIDS) surveillance systems for the purpose of estimating survival time after AIDS diagnosis. The second focuses on the issue of missing data and noncompliance in randomized experiments, where a school choice experiment is used as an illustration. The third looks at handling nonresponse in United States National Health and Nutrition Examination Surveys (NHANES). The emphasis of our review is on the building of imputation models (i.e. the first step), which is the most fundamental aspect of the method.

Statistical Methods in Medical Research, Vol. 8, No. 1, 17-36 (1999)
DOI: 10.1177/096228029900800103


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				J. E. Yoo The Effect of Auxiliary Variables and Multiple Imputation on Parameter Estimation in Confirmatory Factor Analysis Educational and Psychological Measurement, December 1, 2009; 69(6): 929 - 947. [Abstract] [PDF]

				E. A. Stuart, M. Azur, C. Frangakis, and P. Leaf Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative Am. J. Epidemiol., May 1, 2009; 169(9): 1133 - 1139. [Abstract] [Full Text] [PDF]

				S. J. Onufrak, A. Bellasi, F. Cardarelli, V. Vaccarino, P. Muntner, L. J. Shaw, and P. Raggi Investigation of Gender Heterogeneity in the Associations of Serum Phosphorus With Incident Coronary Artery Disease and All-Cause Mortality Am. J. Epidemiol., January 1, 2009; 169(1): 67 - 77. [Abstract] [Full Text] [PDF]

				J. P. Vandenbroucke, E. v. Elm, D. G. Altman, P. C. Gotzsche, C. D. Mulrow, S. J. Pocock, C. Poole, J. J. Schlesselman, M. Egger, and for the STROBE initiative Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration Ann Intern Med, October 16, 2007; 147(8): W-163 - W-194. [Abstract] [Full Text] [PDF]

				C. D. Newgard and R. J. Lewis Effects of Child Age and Body Size on Serious Injury From Passenger Air-Bag Presence in Motor Vehicle Crashes Pediatrics, June 1, 2005; 115(6): 1579 - 1585. [Abstract] [Full Text] [PDF]

				F. Barzi and M. Woodward Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies Am. J. Epidemiol., July 1, 2004; 160(1): 34 - 45. [Abstract] [Full Text] [PDF]

				B. Barnet, C. Arroyo, M. Devoe, and A. K. Duggan Reduced School Dropout Rates Among Adolescent Mothers Receiving School-Based Prenatal Care Arch Pediatr Adolesc Med, March 1, 2004; 158(3): 262 - 268. [Abstract] [Full Text] [PDF]

				A. M. Arnold and R. A. Kronmal Multiple Imputation of Baseline Data in the Cardiovascular Health Study Am. J. Epidemiol., January 1, 2003; 157(1): 74 - 84. [Abstract] [Full Text] [PDF]

				F. P. Rivara, T. D. Koepsell, D. C. Grossman, and C. Mock Effectiveness of Automatic Shoulder Belt Systems in Motor Vehicle Crashes JAMA, June 7, 2000; 283(21): 2826 - 2828. [Abstract] [Full Text] [PDF]