Multiple Imputation for Incomplete Data in Epidemiologic Studies

American Journal of Epidemiology

Volumn 187, Issue 3, 2018, Pages 576-584

  Harel, Ofer ^a   Mitchell, Emily M ^b   Perkins, Neil J ^c   Cole, Stephen R ^d   Tchetgen Tchetgen, Eric J ^e   Sun, Baoluo ^e   Schisterman, Enrique F ^c  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b AGENCY FOR HEALTHCARE RESEARCH AND QUALITY   (United States)

^c EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

^d University of North Carolina at Chapel Hill   (United States)

^e HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

epidemiologic studies; missing data; multiple imputation; parametric methods

Indexed keywords

EPIDEMIOLOGY; NUMERICAL METHOD; PARAMETER ESTIMATION; PREGNANCY; SMOKING;

ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; EPIDEMIOLOGICAL DATA; FEMALE; HIGH RISK PREGNANCY; HUMAN; MATERNAL SMOKING; PATIENT INFORMATION; PREDICTIVE VALUE; PREGNANCY; RISK ASSESSMENT; SPONTANEOUS ABORTION; CLINICAL TRIAL; EPIDEMIOLOGY; MEASUREMENT ACCURACY; MULTICENTER STUDY; STATISTICAL ANALYSIS; STATISTICAL BIAS;

BIAS; DATA ACCURACY; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC RESEARCH DESIGN; EPIDEMIOLOGIC STUDIES; FEMALE; HUMANS; PREGNANCY;

EID: 85042938516     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwx349     Document Type: Article

References (53)

1. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.
2. Stuart EA, Azur M, Frangakis C, et al. Multiple imputation with large data sets: a case study of the Children's Mental Health Initiative. Am J Epidemiol. 2009;169(9):1133-1139.
3. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley &Sons, Inc.; 1987.
4. Harel O, Zhou XH. Multiple imputation: review of theory, implementation and software. Stat Med. 2007;26(16): 3057-3077.
5. Horton NJ, Kleinman KP. Much ado about nothing: a comparison ofmissing datamethods and software to fit incomplete data regression models. Am Stat. 2007;61(1):79-90.
6. Hardy JB. The Collaborative Perinatal Project: lessons and legacy. Ann Epidemiol. 2003;13(5):303-311.
7. Perkins NJ, Cole SR, Harel O, et al. Principled approaches to missing data in epidemiologic studies. Am J Epidemiol. 2018; 187(3):568-575.
8. Bartlett JW, Harel O, Carpenter JR. Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression. Am J Epidemiol. 2015;182(8):730-736.
9. Little RJA, Rubin DB. Statistical Analysis With Missing Data. Hoboken, NJ: John Wiley &Sons, Inc.; 2014.
10. Schafer JL. Analysis of Incomplete Multivariate Data. New York, NY: Chapman &Hall, Inc.; 1997.
11. Lee KJ, Carlin JB. Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation. Am J Epidemiol. 2010;171(5):624-632.
12. van Buuren S, Oudshoorn K. Flexible multivariate imputation by MICE. Leiden, the Netherlands: TNO Prevention and Health; 1999. (TNO-rapport PG 99.054).
13. Raghunathan, TE, Lepkowski, JM, van Hoewyk, J, et al. A multivariate technique formultiply imputing missing values using a series of regression models. SurvMethodol. 2001;27(1):85-95.
14. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res. 2007;16(3):219-242.
15. Little RJA. Missing-data adjustments in large surveys. J Bus Econ Stat. 1988;6(3):287-296.
16. Schenker N, Taylor JMG. Partially parametric techniques for multiple imputation. Comput Stat Data Anal. 1996;22(4):425-446.
17. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377-399.
18. Rodwell L, Lee KJ, Romaniuk H, et al. Comparison of methods for imputing limited-range variables: a simulation study. BMC Med Res Methodol. 2014;14:57.
19. Heckman J. Sample selection bias as a specification error. Econometrica. 1979;47:153-161.
20. Diggle P, Kenward MG. Informative drop-out in longitudinal data analysis [with discussion]. Appl Stat. 1994;43(1):49-93.
21. Little RJA. Pattern-mixture models for multivariate incomplete data. J Am Stat Assoc. 1993;88(421):125-134.
22. Little RJA. A class of pattern-mixture models for normal incomplete data. Biometrika. 1994;81(3):471-483.
23. Little RJA. Modeling the drop-out mechanism in repeatedmeasures studies. J Am Stat Assoc. 1995;90(431):1112-1121.
24. Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics. 1988;44:175-188.
25. Sun BL, Perkins NJ, Cole SR, et al. Inverse-probabilityweighted estimation for monotone and nonmonotone missing data. Am J Epidemiol. 2018;187(3):585-591.
26. Little RJA. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1988, 83(404):1198-1202.
27. Gelman A, Carlin JB, Stern H, et al. Bayesian Data Analysis. 1st ed. London, United Kingdom: Chapman &Hall Ltd.; 1995.
28. Van Buuren S. Flexible Imputation of Missing Data. Boca Raton, FL: Chapman &Hall/CRC Press; 2012.
29. Graham JW. Missing Data: Analysis and Design. New York, NY: Springer Publishing Company; 2012.
30. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods. 2001;6(4):330-351.
31. Siddique J, Harel O, Crespi CM. Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial. Ann Appl Stat. 2012;6(4):1814-1837.
32. Siddique J, Harel O, Crespi CM, et al. Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial. Stat Med. 2014;33(17):3013-3028.
33. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B. 1977;39(1):1-38.
34. Novo AA, Schafer JL. norm: Analysis ofMultivariate Normal Data Sets With Missing Values. (R package, version 1.0-9.2). Vienna, Austria: R Foundation for Statistical Computing; 2013.
35. Barnard J, Rubin DB. Small-sample degrees of freedom with multiple imputation. Biometrika. 1999;86(4):948-955.
36. Lipsitz S, Parzen M, Zhao LP. A degrees-of-freedom approximation in multiple imputation. J Stat Comput Simul. 2002;72(4):309-318.
37. Reiter JP. Small-sample degrees of freedom for multicomponent significance tests with multiple imputation for missing data. Biometrika. 2007;94(2):502-508.
38. Wagstaff DA, Harel O. A closer examination of three smallsample approximations to the multiple-imputation degrees of freedom. Stata J. 2011;11(3):403-419.
39. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
40. SAS Institute Inc. SAS/STAT Software, Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
41. StataCorp LP. Stata Data Analysis Statistical Software: Release 12. College Station, TX: StataCorp LP; 2011.
42. Harel O. Inferences on missing information under multiple imputation and two-stage multiple imputation. Stat Method. 2007;4:75-89.
43. Harel O. Outfluence\-The impact of missing values. Model Assist Stat Appl. 2008;3:161-168.
44. Harel O, Stratton J. Inferences on the outfluence-how do missing values impact your analysis? Commun Stat Theory Methods. 2009;38(16-17):2884-2898.
45. Rubin DB. Inference and missing data. Biometrika. 1976; 63(3):581-592.
46. Daniels MJ, Hogan JW. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Boca Raton, FL: Chapman &Hall/CRC Press; 2008.
47. Graham JW, Olchowski AE, Gilreath TD. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prev Sci. 2007;8(3):206-213.
48. Bodner TE. What improves with increased missing data imputations? Struct Equ Modeling. 2008;15(4):651-675.
49. Horton NJ, Lipsitz SR, Parzen M. A potential for bias when rounding in multiple imputation. Am Stat. 2003;57(4): 229-232.
50. Bernaards CA, Belin TR, Schafer JL. Robustness of a multivariate normal approximation for imputation of incomplete binary data. Stat Med. 2007;26(6):1368-1382.
51. Wagstaff DA, Kranz S, Harel O. A preliminary study of active compared with passive imputation of missing body mass index values among non-Hispanic white youths. Am J Clin Nutr. 2009;89(4):1025-1030.
52. Meng XL. Multiple-imputation inferences with uncongenial sources of input. Stat Sci. 1994;9(4):538-558.
53. Carpenter JR, Kenward MG. Multiple Imputation and Its Application. Chichester, United Kingdom: John Wiley &Sons Ltd.; 2013.