67
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies

, , , , , & show all
Pages 1543-1570 | Received 23 Aug 2022, Accepted 05 Dec 2023, Published online: 18 Dec 2023

References

  • He Y, Zhang G, Hsu CH. Multiple imputation of missing data in practice: basic theory and analysis strategies. 1st ed. Chapman and Hall/CRC Press; 2021.
  • Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2014.
  • Rubin DB. Multiple imputations in sample surveys—a phenomenological Bayesian approach to nonresponse. In Proceedings of the survey research methods section of the American Statistical Association; 1978. p. 20–34.
  • Aßmann C, Würbach A, Goßmann S, et al. Nonparametric multiple imputation for questionnaires with individual skip patterns and constraints: the case of income imputation in the national educational panel study. Sociol Methods Res. 2017;46(4):864–897. doi:10.1177/0049124115610346
  • Arslanturk S, Siadat M, Ogunyemi T, et al. Skip pattern analysis for detection of undetermined and inconsistent data. In: 2012 5th international conference on biomedical engineering and informatics; 2012. p. 1122–1126. doi:10.1109/BMEI.2012.6513221
  • Lin TH. Estimating latent class model parameters for filter questions with skip patterns. Qual Quant. 2012;46:545–552. doi:10.1007/s11135-010-9385-x
  • Manski CF, Molinari F. Skip sequencing: a decision problem in questionnaire design. Ann Appl Stat. 2008;2(1):264–285. doi:10.1214/07-AOAS134
  • Irimata KE, He Y, Cai B, et al. Comparison of quarterly and yearly calibration data for propensity score adjusted web survey estimates. Surv Methods Insights Field. 2020. Special issue ‘Advancements in Online and Mobile Survey Methods’. doi:10.13094/SMIF-2020-00018externalicon
  • Parker J, Miller K, He Y, et al. Overview and initial results of the national center for health statistics’ research and development survey. Stat J IAOS. 2020;36(4):1199–1211. doi:10.3233/SJI-200678
  • He Y, Cai B, Shin H-C, et al. The national center for health statistics’ 2015 and 2016 research and development surveys. National Center for Health Statistics. Vital Health Stat. 2020;1(64). https://www.cdc.gov/nchs/data/series/sr_01/sr01-64-508.pdfpdf icon.
  • Hapfelmeier A, Hothorn T, Ulm K. Recursive partitioning on incomplete data using surrogate decisions and multiple imputation. Comput Stat Data Anal. 2012;56:1552–1565. doi:10.1016/j.csda.2011.09.024
  • David M, Little RJA, Samuhel ME, et al. Alternative methods for CPS income imputation. J Am Stat Assoc. 1986;81(393):29–41. doi:10.1080/01621459.1986.10478235
  • National Center for Health Statistics. multiple imputation of family income in 2020 national health interview survey: methods; 2021. September 2020. Available from https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2020/NHIS2020-imputation-techdoc-508.pdf
  • Schenker N, Raghunathan TE, Chiu P-L, et al. Multiple imputation of missing income data in the national health interview survey. J Am Stat Assoc. 2006;101:924–933. doi:10.1198/016214505000001375
  • Zarnoch SJ, Cordell HK, Betz CJ, et al. Multiple imputation: an application to income nonresponse in the national survey on recreation and the environment. In: Research paper SRS-49. Asheville (NC): U.S. Department of Agriculture Forest Service, Southern Research Station; 2010. p. 15.
  • Watson N, Starick R. Evaluation of alternative income imputation methods for a longitudinal survey. J Off Stat. 2011;27:693–715.
  • Raghunathan TE, Lepkowski JM, van Hoewyk J, et al. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol. 2001;27:85–95.
  • Raghunathan TE, Solenberger PW, Van Hoewyk J. IV eware: imputation and variation estimation software user guide. Ann Arbor (MI): Institute for Social Research; 2002.
  • He Y, Zaslavsky AM, Harrington DP, et al. Multiple imputation in a large-scale complex survey: a practical guide. Stat Methods Med Res. 2010;19:653–670. doi:10.1177/0962280208101273
  • Deng Y, Chang C, Ido MS, et al. Multiple imputation for general missing data patterns in the presence of high-dimensional data. Sci Rep. 2016;6(6):21689), doi:10.1038/srep21689
  • SAS Institute Inc. SAS/STAT® 14.1 user’s guide. Cary (NC): SAS Institute Inc; 2015.
  • van Buuren S, Groothuis Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–67.
  • Meng XL. Multiple-imputation inferences with uncongenial sources of input: rejoinder. Stat Sci. 1994;9:566–573.
  • Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc. 1996;91(434):473–489. doi:10.1080/01621459.1996.10476908
  • Kim JK, Brick JM, Fuller WA, et al. On the bias of the multiple-imputation variance estimator in survey sampling. J R Stat Soc Ser B Stat Methodol. 2006;68(3):509–521. doi:10.1111/j.1467-9868.2006.00546.x Available from: http://www.jstor.org/stable/3879288.
  • Zhou H, Elliott MR, Raghunathan TE. A two-step semiparametric method to accommodate sampling weights in multiple imputation. Biometrics. 2016;72:242–252. doi:10.1111/biom.12413
  • Quartagno M, Carpenter JR, Goldstein H. Multiple imputation with survey weights: a multilevel approach. J Surv Stat Methodol. 2020;8(5):965–989. doi:10.1093/jssam/smz036
  • Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. Br Med J. 2009;338:b2393. doi:10.1136/bmj.b2393. PMID: 19564179; PMCID: PMC2714692.
  • Hayati Rezvan P, Lee KJ, Simpson JA. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol. 2015 Apr 7;15:30. doi:10.1186/s12874-015-0022-1. PMID: 25880850; PMCID: PMC4396150.
  • van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res. 2007;16:219–242. doi:10.1177/0962280206074463
  • Tourangeau R, Frederick GC, Couper MP. The science of web surveys. Oxford: Oxford University Press; 2013.
  • Baker R, Brick JM, Bates NA, et al. Summary report of AAPOR task force on non-probability sampling. J Surv Stat Methodol. 2013;1:90–143. doi:10.1093/jssam/smt008

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.