Skip to main content
SpringerLink
Log in
Book cover

Modern Issues and Methods in Biostatistics pp 117–143Cite as

Missing Data Imputation and Analysis

  • Chapter
  • First Online:

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Missing data are a common occurrence in scientific research and in our daily lives. In a survey, a lack of response constitutes missing data. In clinical trials, missing data can be caused by a patient’s refusal to continue in a study, treatment failures, adverse events, or patient relocations.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Further Readings and References

  • Carpenter J.R., Kenward, M.G., White, I.R.: Sensitivity analysis after multiple imputation under missing at random: A weighting approach. Stat. Methods Med. Res. 16, 259–275 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  • CHMP: Guideline on missing data in confirmatory clinical trials/1776/99 Rev. 1. www.ema.europa.eu (2009). Accessed 8 Aug 2010

  • DeGruttola, V., Tu, X.M.: Modeling progression of CD4 + lymphocyte count and its relationship to survival time. Biometrics 50, 1003–1014 (1994)

    Article  Google Scholar 

  • Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood estimation from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  • Dmitrienko, A., Molenberghs, G., Chuang-Stein, C., Often, W.: Analysis of Clinical Trials Using SAS. SAS Institute, Cary (2005)

    Google Scholar 

  • Diggle P., Kenward, M.G.: Informative drop-out in longitudinal data analysis. Appl. Stat. 43, 49–93 (1994)

    Article  MATH  Google Scholar 

  • Gao, S.: A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data. Stat. Med. 23, 211–219 (2004)

    Article  Google Scholar 

  • Gao, S., Hui, S.L.: Estimating the incidence of dementia from two-phase sampling with non-ignorable missing data. Stat. Med. 19, 1545–554 (2000)

    Article  Google Scholar 

  • Harel, O., Zhou, X.H.: Multiple imputation: Review of theory, implementation and software. Stat. Med. 26, 3057–3077 (2007)

    Article  MathSciNet  Google Scholar 

  • Henderson, R., Diggle, P., Dobson, A.: Joint modeling of longitudinal measurements and event time data. Biostatistics 1, 465–480 (2000)

    Article  MATH  Google Scholar 

  • Hogan, J.W., Laird, N.M.: Model-based approaches to analysing incomplete longitudinal and failure time data. Stat. Med. 16, 259–272 (1997)

    Article  Google Scholar 

  • Hogan, J.W., Roy, J., Korkontzelou C.: Biostatistics tutorial: Handling dropout in longitudinal data. Stat. Med. 23, 1455–1497 (2004)

    Article  Google Scholar 

  • Horvitz, D.G., Thompson, D.J.: A Generalization of sampling without replacement from a finite universe, J. Am. Stat. Assoc. 47, 663–685 (1952)

    Article  MATH  MathSciNet  Google Scholar 

  • Lancaster, T., Intrator, O.: Panel data with survival: Hospitalization of HIV-positive patients. J. Am. Stat. Assoc. 93, 46–53 (1998)

    Article  MATH  Google Scholar 

  • Little, R.J.A.: Pattern-mixture models for multivariate incomplete data. J. Am. Stat. Assoc. 88, 125–134 (1993)

    Article  MATH  Google Scholar 

  • Little, R.J.A.: Modeling the drop-out mechanism in repeated-measure studies. J. Am. Stat. Assoc. 90, 1112–1121 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  • Little, R.J.: Panel on Handling Missing Data in Clinical Trial: The Prevention and Treatment of Missing Data in Clinical Trials. The National Academies Press, Washington (2010)

    Google Scholar 

  • Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. Wiley, New York (2002)

    MATH  Google Scholar 

  • Liu, J.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2003)

    Google Scholar 

  • Molenberghs, G., Kenward, M.G.: Missing Data in Clinical Studies. Wiley, Chichester (2008)

    Google Scholar 

  • Müller, U.U., Schick, A., Wefelmeyer, W.: Imputing responses that are not missing. In: Nikulin, M., Commengs, D., Huber, C. (eds.) Probability, Statistics and Modeling in Public Health. Springer, New York (2006)

    Google Scholar 

  • Rubin D.B.: Inference and missing data. Biometrika 63, 581–592 (1976)

    MATH  Google Scholar 

  • Rubin D.B.: Multiple Imputation for Nonresponse in Surveys. Wiley, New York (1987)

    Book  Google Scholar 

  • SAS Institute: SAS/STAT 9.1 User’s Guide, vol. 1–7. SAS Institute, Gary (2004)

    Google Scholar 

  • SAS Institute Inc.: SAS/STAT 9.2 User’s Guide. Cary, NC: SAS Institute Inc. (2008)

    Google Scholar 

  • Ten Have, T.R., Kunselman, A.R., Pulkstenis, E.P., Landis, J.R. Mixed effect logistic regression models for longitudinal binary response data with informative drop out. Biometrics 54, 367–383 (1998)

    Article  MATH  Google Scholar 

  • Tsiatis, A.A.: Semiparametric Theory and Missing Data. Springer, New York (2009)

    Google Scholar 

  • Van Der Laan, M.J., Robins, J.M.: Unified Methods for Censored Longitudinal Data and Causality. Springer, New York (2003)

    MATH  Google Scholar 

  • Vonesh, E.F., Greene, T., Schluchter, M.D.: Shared parameter models for the joint analysis of longitudinal data and event times. Stat. Med. 25, 143–163 (2006)

    Article  MathSciNet  Google Scholar 

  • Yang, X., Li, J., Shoptaw, S.: Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values. Stat. Med. 27, 2826–2849 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biometrics, AMAG Pharmaceuticals, Inc., Hayden Ave. 100, 02421, Lexington, Massachusetts, USA

    Mark Chang

Authors
  1. Mark Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Chang, M. (2011). Missing Data Imputation and Analysis. In: Modern Issues and Methods in Biostatistics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9842-2_5

Download citation

Publish with us

Policies and ethics

Search

Navigation