Advanced Statistics: Missing Data in Clinical Research-Part 2: Multiple Imputation

Academic Emergency Medicine

Volumn 14, Issue 7, 2007, Pages 669-678

  Newgard, Craig D ^a   Haukoos, Jason S ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

bias; clinical research; imputation; missing data; multiple imputation; statistical analysis

Indexed keywords

ARTICLE; CLINICAL RESEARCH; CODING; COMPUTER PROGRAM; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; DATA BASE; HUMAN; INFORMATION PROCESSING; METHODOLOGY; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

ACCIDENTS, TRAFFIC; BIOMEDICAL RESEARCH; DATA INTERPRETATION, STATISTICAL; HUMANS; LOGISTIC MODELS; RESEARCH DESIGN; SOFTWARE; STATISTICS;

EID: 34250614356     PISSN: 10696563     EISSN: None     Source Type: Journal    
DOI: 10.1197/j.aem.2006.11.038     Document Type: Article

References (38)

1. Haukoos J.S., and Newgard C.D. Advanced statistics: missing data in clinical research-part 1: an introduction and conceptual framework. Acad Emerg Med 14 (2007) 662-668
2. Rubin D.B. Inference and missing data. Biometrika 63 (1976) 581-592
3. Rubin D.B. Multiple Imputation for Nonresponse in Surveys (1987), Wiley, New York
4. Houck P.R., Mazumdar S., Koru-Sengul T., et al. Estimating treatment effects from longitudinal clinical trial data with missing values: comparative analyses using different methods. Psychiatr Res 129 (2004) 209-215
5. Liu G., and Gould A.L. Comparison of alternative strategies for analysis of longitudinal trials with dropouts. J Biopharm Stat 12 (2002) 207-226
6. Tang L., Song J., Belin T.R., and Unutzer J. A comparison of imputation methods in a longitudinal randomized clinical trial. Stat Med 24 (2005) 2111-2128
7. Liu M., Taylor J.M.G., and Belin T.R. Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics 56 (2000) 1157-1163
8. Demirtas H. Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out. Stat Med 24 (2005) 2345-2363
9. Landrum M.B., and Becker M.P. A multiple imputation strategy for incomplete longitudinal data. Stat Med 20 (2001) 2741-2760
10. Pan W. A multiple imputation approach to regression analysis for doubly censored data with application to AIDS studies. Biometrics 57 (2001) 1245-1250
11. Taylor J.M.G., Cooper K.L., Wei J.T., Sarma A.V., Raghunathan T.E., and Heeringa S.G. Use of multiple imputation to correct for nonresponse bias in a survey of urologic symptoms among African-American men. Am J Epidemiol 156 (2002) 774-782
12. Kmetic A., Joseph L., Berger C., and Tenenhouse A. Multiple imputation to account for missing data in a survey: estimating the prevalence in osteoporosis. Epidemiology 13 (2002) 437-444
13. Greenland S., and Finkle W.D. A critical look at methods for handling missing covariates in epidemiologic regression analyses. Am J Epidemiol 142 (1995) 1255-1264
14. Joseph L., Belisle P., Tamim H., and Sampalis J.S. Selection bias found in interpreting analyses with missing data for the prehospital index for trauma. J Clin Epidemiol 57 (2004) 147-153
15. Newgard C.D. The validity of using multiple imputation for missing out-of-hospital data in a state trauma registry. Acad Emerg Med 13 (2006) 314-324
16. Reiter J.P., and Raghunathan T.E. Multiple imputation for missing data in surveys with complex sample designs. Technical Report, Institute for Social Research (2002), University of Michigan, Ann Arbor, MI
17. Xie F., and Paik M.C. Multiple imputation methods for the missing covariates in generalized estimating equation. Biometrics 53 (1997) 1538-1546
18. Wu H., and Wu L. A multiple imputation method for missing covariates in non-linear mixed-effects models with application to HIV dynamics. Stat Med 20 (2001) 1755-1769
19. Crawford S.L., Tennstedt S.L., and McKinlay J.B. A comparison of analytic methods for non-random missingness of outcome data. J Clin Epidemiol 48 (1995) 209-219
20. Sinharay S., Stern H.S., and Russell D. The use of multiple imputation for the analysis of missing data. Psychol Methods 6 (2001) 317-329
21. van der Heijden G.J.M.G., Donders A.R.T., Stijnen T., and Moons K.G.M. Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59 (2006) 1102-1109
22. Wood A.M., White I.R., and Thompson S.G. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials 1 (2004) 368-376
23. Little R.J.A., and Rubin D.B. Statistical Analysis with Missing Data. 2nd ed. (2002), Wiley, Hoboken, NJ
24. Raghunathan T.E. What do we do with missing data? Some options for analysis of incomplete data. Annu Rev Public Health 25 (2004) 99-117
25. Schafer J.L. Analysis of Incomplete Multivariate Data (1997), Chapman & Hall, Boca Raton, FL
26. Raghunathan T.E., Lepkowski J.M., Van Hoewyk J., and Solenberger P.W. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodol 27 (2001) 85-95
27. Raghunathan TE, Solenberger PW, Van Hoewyk JV. IVEware: Imputation and variance estimation software user guide. Survey Methodology Program, Institute for Social Research, University of Michigan, Mar 2002.
28. Gelman A., and Rubin D.B. Markov chain Monte Carlo methods in biostatistics. Stat Methods Med Res 5 (1996) 339-355
29. Horton N.J., and Lipsitz S.R. Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am Stat 55 (2001) 244-254
30. Moons K.G.M., Donders R.A.R.T., Stijnen T., and Harrell F.E. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 59 (2006) 1092-1101
31. Collins L.M., Schafer J.L., and Kam C.M. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods 6 (2001) 330-351
32. Auleley G.R., Giraudeau B., Baron G., Maillefert J.F., Dougados M., and Ravaud P. The methods for handling missing data in clinical trials influence sample size requirements. J Clin Epidemiol 57 (2004) 447-453
33. Shafer J.L. Multiple imputation: a primer. Stat Methods Med Res 8 (1999) 3-15
34. Rubin D.B., and Schenker N. Multiple imputation in health-care databases: an overview and some applications. Stat Med 10 (1991) 585-598
35. Allison P.D. Missing Data. Sage University Papers Series on Quantitative Applications in the Social Sciences (2001), Sage, Thousand Oaks, CA pp 7-136
36. Allison P.D. Missing data techniques for structural equation modeling. J Abnorm Psychol 112 (2003) 545-557
37. Barnard J., and Meng X.L. Applications of multiple imputation in medical studies: from AIDS to NHANES. Stat Methods Med Res 8 (1999) 17-36
38. Yuan YC. Multiple imputation for missing data: concepts and new development. P267-25. Rockville, MD: SAS Institute Inc.