Missing data and multiple imputation in clinical epidemiological research

Clinical Epidemiology

Volumn 9, Issue , 2017, Pages 157-166

  Pedersen, Alma B ^a   Mikkelsen, Ellen M ^a   Cronin Fenton, Deirdre ^a   Kristensen, Nickolaj R ^a   Pham, Tra My ^b   Pedersen, Lars ^a   Petersen, Irene ^a,b  

^a AARHUS UNIVERSITY HOSPITAL   (Denmark)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

MAR; MCAR; Missing data; MNAR; Multiple imputation; Observational study

Indexed keywords

CASE STUDY; OBSERVATIONAL STUDY; PROGNOSIS; SOFTWARE; UNCERTAINTY; VALIDITY;

EID: 85015735233     PISSN: None     EISSN: 11791349     Source Type: Journal    
DOI: 10.2147/CLEP.S129785     Document Type: Article

References (29)

1. Marston L, Carpenter JR, Walters KR, Morris RW, Nazareth I, Petersen I. Issues in multiple imputation of missing data for large general practice clinical databases. Pharmacoepidemiol Drug Saf. 2010;19(6):618–626.
2. Pedersen AB, Baggesen LM, Ehrenstein V, Pedersen L, Lasgaard M, Mikkelsen EM. Perceived stress and risk of any osteoporotic fracture. Osteoporos Int. 2016;27(6):2035–2045.
3. Buuren Sv. Flexible Imputation of Missing Data. Interdisciplinary Statistics Series. Boca Raton, FL: Chapman & Hall/CRC; 2012.
4. 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.
5. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549–576.
6. Rubin DB. Inference and missing data. Biometrika. 1976;63(3):581–592.
7. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59(10):1087–1091.
8. Little RJA. A test of missing completelly at random for multivariate data with missing values. J Am Stat Assoc. 1988;83(404):1198–1202.
9. Mohan K, Pearl J, Tian J. Graphical models for inference with missing data. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, editors. Advances in Neural Information Processing System 26 (NIPS-2013). Red Hook, NY: Curran Associates, Inc.; 2013:1277–1285.
10. Cappelleri JC, Zou KH, Bushmakin A, Alvir MJM, Symonds T. Patient-Reported Outcomes: Measurement, Implementation and Interpretation. Boca Raton, FL: CRC Press; 2013:2013.
11. Wisniewski SR, Leon AC, Otto MW, Trivedi MH. Prevention of missing data in clinical research studies. Biol Psychiatry. 2006;59(11):997–1000.
12. Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analyses. Am J Epidemiol. 1995;142(12):1255–1264.
13. Little RJA. Regression with missing X’s: a review. J Am Stat Assoc. 1992;87(420):1227–1237.
14. Apold H, Meyer HE, Espehaug B, Nordsletten L, Havelin LI, Flugsrud GB. Weight gain and the risk of total hip replacement a population-based prospective cohort study of 265,725 individuals. Osteoarthritis Cartilage. 2011;19(7):809–815.
15. Pedersen AB, Sorensen HT, Mehnert F, Overgaard S, Johnsen SP. Risk factors for venous thromboembolism in patients undergoing total hip replacement and receiving routine thromboprophylaxis. J Bone Joint Surg Am. 2010;92-A(12):2156–2164.
16. Barnes SA, Larsen MD, Schroeder D, Hanson A, Decker PA. Missing data assumptions and methods in a smoking cessation study. Addiction. 2010;105(3):431–437.
17. Klebanoff MA, Cole SR. Use of multiple imputation in the epidemiologic literature. Am J Epidemiol. 2008;168(4):355–357.
18. Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc. 1996; 91(434):473–489.
19. Carpenter J, Kenward M. Multiple Imputation and Its Application. New York, NY: John Wiley & Sons; 2013.
20. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–177.
21. 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.
22. Moons KG, Donders RA, Stijnen T, Harrell FE Jr. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol. 2006;59(10):1092–1101.
23. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4): 377–399.
24. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res. 2007;16(3):219–242.
25. StataCorp. Stata: Release 13. Statistical Software. College Station, TX: StataCorp LP; 2013. Available from: https://www.stata.com/manuals13/mi.pdf. Accessed December 1, 2016.
26. Rubin DB. Introduction in Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ: John Wiley & Sons, Inc; 1987.
27. 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.
28. Bodner TE. What improves with increased missing data imputations? Struct Equ Modeling. 2008;15(4):651–675.
29. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vanden-broucke JP; STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596): 1453–1457.