When and how should multiple imputation be used for handling missing data in randomised clinical trials - A practical guide with flowcharts

BMC Medical Research Methodology

Volumn 17, Issue 1, 2017, Pages

  Jakobsen, Janus Christian ^a,b   Gluud, Christian ^a   Wetterslev, Jørn ^a   Winkel, Per ^a  

^a Rigshospitalet   (Denmark)

^b HOLBÆK HOSPITAL   (Denmark)

Author keywords

Missing data; Multiple imputation; Randomised clinical trials

Indexed keywords

HUMAN; MEASUREMENT ACCURACY; PROCEDURES; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL ANALYSIS; STATISTICAL MODEL;

DATA ACCURACY; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 85037664373     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/s12874-017-0442-1     Document Type: Article

References (33)

1. Groenwold RHH, Moons KGM, Vandenbroucke JP. Randomized trials with missing outcome data: how to analyze and what to report. CMAJ Can Med Assoc J. 2014;186(15):1153-7.
2. Nguyen TL, Collins GS, Lamy A, Devereaux PJ, Daures JP, Landais P, Le Manach Y. Simple randomization did not protect against bias in smaller trials. J Clin Epidemiol. 2017;84:105-13.
3. Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, Murphy SA, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367(14):1355-60.
4. Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR: Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:157-60.
5. Dziura JD, Post LA, Zhao Q, Fu Z, Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med. 2013;86(3):343-58.
6. Morris TP, Kahan BC, White IR. Choosing sensitivity analyses for randomised trials: principles. BMC Med Res Methodol. 2014;14:11.
7. Skoog M, Saarimäki JM, Gluud C, Sheinin M, Erlendsson K, Aamdal S, et al. Transparency and registration in clinical research in the Nordic countries. Oslo: NordForsk: Nordic Trial Alliance; 2015. p. 1-108.
8. Ebrahim S, Sohani ZN, Montoya L, Agarwal A, Thorlund K, Mills EJ, Ioannidis JP. Reanalyses of randomized clinical trial data. JAMA. 2014;312(10):1024-32.
9. Mayo-Wilson E, Fusco N, Li T, Hong H, Canner JK, Dickersin K, Investigators M. Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis. J Clin Epidemiol. 2017;86:39-50.
10. Mayo-Wilson E, Li T, Fusco N, Bertizzolo L, Canner JK, Cowley T, Doshi P, Ehmsen J, Gresham G, Guo N et al: Cherry-picking by trialists and meta-analysts can drive conclusions about intervention efficacy. J Clin Epidemiol. 2017;91:95-110.
11. Kahan BC, Morris TP. Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis. BMJ. 2012;345:e5840.
12. Kahan BC, Morris TP. Improper analysis of trials randomised using stratified blocks or minimisation. Stat Med. 2011;31:328-40.
13. Bell ML, Fiero M, Horton NJ, Hsu CH. Handling missing data in RCTs; a review of the top medical journals. BMC Med Res Methodol. 2014;14:118.
14. Hróbjartsson A, Thomsen ASS, Emanuelsson F, Tendal B, Hilden J, Boutron I, Ravaud P, Brorson S. Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. BMJ. 2012;344:e1119.
15. Hróbjartsson A, Thomsen ASS, Emanuelsson F, Tendal B, Hilden J, Boutron I, Ravaud P, Brorson S. Observer bias in randomized clinical trials with measurement scale outcomes: a systematic review of trials with both blinded and nonblinded assessors. CMAJ. 2013;185(4):E201-11.
16. Hrobjartsson A, Emanuelsson F, Skou Thomsen AS, Hilden J, Brorson S. Bias due to lack of patient blinding in clinical trials. A systematic review of trials randomizing patients to blind and nonblind sub-studies. Int J Epidemiol. 2014;43(4):1272-83.
17. Lundh A, Lexchin J, Mintzes B, Scholl JB, Bero L: Industry sponsorship and research outcome. Cochrane Database Syst Rev 2017, Art. No.: MR000033. DOI: https://doi.org/10.1002/14651858.MR000033.pub3.(2):MR000033.
18. Zhang Y, Alyass A, Vanniyasingam T, Sadeghirad B, Florez ID, Pichika SC, Kennedy SA, Abdulkarimova U, Zhang Y, Iljon T, et al. A systematic survey of the methods literature on the reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials. J Clin Epidemiol. 2017;88:67-80.
19. Jorgensen AW, Lundstrom LH, Wetterslev J, Astrup A, Gotzsche PC. Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial. PLoS One. 2014;9(11):e111964.
20. Garson GD: Missing Values Analysis and Data Imputation. North Carolina: Statistical Associates Publishers: Statistical Associates Publishers; 2015.
21. Horton NJ, Lipsitz SR. Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am Stat. 2001;55(3):244-54.
22. StataCorp. Stata: release 14. Statistical software. College Station, TX: StataCorp LP; 2015.
23. Jakobsen JC, Gluud C, Winkel P, Lange T, Wetterslev J. The thresholds for statistical and clinical significance - a five-step procedure for evaluation of intervention effects in randomised clinical trials. BMC Med Res Methodol. 2014;14:34.
24. Jakobsen JC, Wetterslev J, Winkel P, Lange T, Gluud C. Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods. BMC Med Res Methodol. 2014;14(1):120.
25. Perner A, Haase N, Guttormsen AB, Tenhunen J, Klemenzson G, Aneman A, Madsen KR, Moller MH, Elkjaer JM, Poulsen LM, et al. Hydroxyethyl starch 130/0.42 versus Ringer's acetate in severe sepsis. N Engl J Med. 2012;367(2):124-34.
26. Clark TG, Altman DG. Developing a prognostic model in the presence of missing data: an ovarian cancer case study. J Clin Epidemiol. 2003;56(1):28-37.
27. Higgins JPT, Green S: The Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0. The Cochrane Collaboration 2011, Available from www.cochrane-handbook.org. Accessed 1 Oct 2017.
28. Allison PD. Handling missing data by maximum likelihood, statistical horizons. In: SAS global forum 2012 statistics and data analysis; 2012.
29. StataCorp.: STATA Multiple imputation. Reference manual 2013, Release 13.
30. U.S. Department of Health and Human Services Food and Drug Administration. Guidance for industry E9 statistical principles for clinical trials. In: Center for Biologics Evaluation and Research (CBER); 1998.
31. Kahan BC. Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how? BMC Med Res Methodol. 2014;14:20.
32. Locascio JJ, Atri A. An overview of longitudinal data analysis methods for neurological research. Dement Geriatr Cogn Dis Extra. 2011;1(1):330-57.
33. Birhanu T, Molenberghs G, Sotto C, Kenward MG. Doubly robust and multiple-imputation-based generalized estimating equations. J Biopharm Stat. 2011;21(2):202-25.