Skip to main content
SpringerLink
Log in

Comments on: Missing data methods in longitudinal studies: a review

  • Discussion
  • Published:
TEST Aims and scope Submit manuscript

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Carpenter JR, Goldstein H (2004) Multiple imputation in MLwiN. Multilev Model Newsl 16(2):9–18

    Google Scholar 

  • Carpenter JR, Kenward MG (2008) Missing data in randomised controlled trials—a practical guide. Birmingham: National Institute for Health Research, Publication RM03/JH17/MK. Available at http://www.haps.bham.ac.uk/publichealth/methodology/projects/RM03_JH17_MK.shtml. Accessed 29 Jan 2009

  • Carpenter JR, Kenward MG, White IR (2007) Sensitivity analysis after multiple imputation under missing at random: a weighting approach. Stat Methods Med Res 16(3):259–275

    Article  MATH  MathSciNet  Google Scholar 

  • Goldstein H, Carpenter JR, Carpenter MG, Levin K (2009) Multilevel models with multivariate mixed response types. Stat Model (in press)

  • Little RJA, Yau L (1996) Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52(4):1324–1333

    Article  MATH  Google Scholar 

  • Liu M, Taylor JMG, Belin TR (2000) Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics 56(4):1157–1163

    Article  MATH  MathSciNet  Google Scholar 

  • Molenberghs G, Kenward MG (2007) Missing data in clinical studies. Wiley, Chichester

    Book  Google Scholar 

  • Ng ESW, Carpenter JR, Goldstein H, Rasbash J (2006) Estimation in generalised linear mixed models with binary outcomes by simulated maximum likelihood. Stat Model 6(1):23–42

    Article  MathSciNet  Google Scholar 

  • Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York

    Book  Google Scholar 

  • Schafer JL (1997a) Analysis of incomplete multivariate data. Chapman & Hall, London

    MATH  Google Scholar 

  • Schafer JL (1997b) Imputation of missing covariates under a general linear mixed model. Technical report, Department of Statistics, Penn State University

  • van Buuren S (2007) Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res 16(3):219–242

    Article  MATH  MathSciNet  Google Scholar 

  • White I, Carpenter JR, Evans S, Schroter S (2007) Eliciting and using expert opinions about non-response bias in randomised controlled trials. Clin Trials 4:125–139

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Statistics Unit, London School of Hygiene and Tropical Medicine, London, UK

    Michael G. Kenward & James R. Carpenter

Authors
  1. Michael G. Kenward
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James R. Carpenter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael G. Kenward.

Additional information

This comment refers to the invited paper available at: http://dx.doi.org/10.1007/s11749-009-0138-x.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kenward, M.G., Carpenter, J.R. Comments on: Missing data methods in longitudinal studies: a review. TEST 18, 65–67 (2009). https://doi.org/10.1007/s11749-009-0143-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11749-009-0143-0

Keywords

Search

Navigation