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Clinical Epidemiology Volume 14, 2022 - Issue
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ORIGINAL RESEARCH

Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

Chinchin Wang1 Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada;2 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, H3A 1A2, CanadaView further author information
,
Tyrel Stokes3 Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 0B9, CanadaView further author information
,
Russell J Steele3 Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 0B9, CanadaView further author information
,
Niels Wedderkopp4 Orthopedic Department University Hospital of South West Denmark, Department of Regional Health Research, University of Southern Denmark, Odense, DenmarkORCID Iconhttps://orcid.org/0000-0002-9660-6618View further author information
ORCID Icon &
Ian Shrier1 Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, CanadaCorrespondence[email protected]
View further author information
Pages 1387-1403 | Received 30 Mar 2022, Accepted 09 Nov 2022, Published online: 15 Nov 2022

References

  • Perkins NJ, Cole SR, Harel O., et al. Principled approaches to missing data in epidemiologic studies. Am J Epidemiol. 2018;187(3):568–575. doi:10.1093/aje/kwx348
  • Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013;64:402–406. doi:10.4097/kjae.2013.64.5.402
  • Wedderkopp N, Jespersen E, Franz C, et al. Study protocol. The Childhood Health, Activity, and Motor Performance School Study Denmark (The CHAMPS-study DK). BMC Pediatr. 2012;12:128. doi:10.1186/1471-2431-12-128
  • Little RJA, Rubin DB. Statistical Analysis with Missing Data, Third Edition [Internet]. 1st ed. John Wiley & Sons, Ltd; 2019. Available from 10.1002/9781119482260.
  • Little RJA, Rubin DB. Complete-Case and Available-Case Analysis, Including Weighting Methods. Stat Anal Missing Data [Internet]. John Wiley & Sons, Ltd; 2014:41–58. Available from 10.1002/9781119013563.ch3.
  • Newman DA. Missing Data: five Practical Guidelines. Organ Res Methods. 2014;17:372–411. doi:10.1177/1094428114548590
  • Pedersen AB, Mikkelsen EM, Cronin-Fenton D, et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol. 2017;9:157–166. doi:10.2147/CLEP.S129785
  • Sterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi:10.1136/bmj.b2393
  • Donders ART, van der Heijden GJMG, Stijnen T, Moons KGM. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59:1087–1091. doi:10.1016/j.jclinepi.2006.01.014
  • Rubin DB. Discussion on Multiple Imputation. Int Stat Rev Rev Int Stat. 2003;71:619–625. doi:10.1111/j.1751-5823.2003.tb00216.x
  • Horton NJ, Kleinman KP. Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models. Am Stat. 2007;61:79–90. doi:10.1198/000313007X172556
  • White IR, Carlin JB. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Stat Med. 2010;29:2920–2931. doi:10.1002/sim.3944
  • Andridge RR, Little RJA. A Review of Hot Deck Imputation for Survey Non-response. Int Stat Rev Rev Int Stat. 2010;78:40–64. doi:10.1111/j.1751-5823.2010.00103.x
  • Rubin DB. The Bayesian Bootstrap. Ann Stat. 1981;9:130–134. doi:10.1214/aos/1176345338
  • Vaughan LK, Divers J, Padilla M, et al. The use of plasmodes as a supplement to simulations: a simple example evaluating individual admixture estimation methodologies. Comput Stat Data Anal. 2009;53:1755–1766. doi:10.1016/j.csda.2008.02.032
  • Cattell RB, Jaspers J. A general plasmode (No. 30-10-5-2) for factor analytic exercises and research. Multivar Behav Res Monogr. 1967;67–3:211.
  • Drechsler J. Multiple imputation in practice—a case study using a complex German establishment survey. AStA Adv Stat Anal. 2011;95:1–26. doi:10.1007/s10182-010-0136-z
  • Van Buuren S, Groothuis-Oudshoorn K. Multivariate imputation by chained equations. J Stat Softw. 2011;45:1–67.
  • R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2021. Available from: http://www.R-project.org. Accessed November 11, 2022.
  • Van Buuren S. Flexible Imputation of Missing Data. CRC Press; 2018.
  • McHugh ML. Interrater reliability: the kappa statistic. Biochem Medica. 2012;22:276–282. doi:10.11613/BM.2012.031
  • Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J Clin Epidemiol. 1993;46:423–429. doi:10.1016/0895-4356(93)90018-V
  • Little RJ, Yosef M, Cain KC, Nan B, Harlow SD. A hot-deck multiple imputation procedure for gaps in longitudinal data on recurrent events. Stat Med. 2008;27:103–120. doi:10.1002/sim.2939
  • Wang C-N, Little R, Nan B, Harlow SD, Hot-Deck Multiple A. Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories. Biometrics. 2011;67:1573–1582. doi:10.1111/j.1541-0420.2011.01558.x
  • Graham JW, Olchowski AE, Gilreath TD. How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory. Prev Sci. 2007;8:206–213. doi:10.1007/s11121-007-0070-9

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