Missing Data Methods in HIV Clinical Trials: Regulatory Guidance And Alternative Approaches


Efficacy in HIV clinical trials is measured by changes in HIV RNA levels over time as well as the proportion of subjects with HIV RNA levels below an assay’s threshold of reliable quantification at a single time point. Missing data arise naturally due to missed visits and premature discontinuations of treatment. The available data are then analyzed using repeated measures models and univariate comparisons of proportions, assuming missing data occur at random or considering missing values as treatment failures (worst case scenario). These and other methods recently proposed by regulatory authorities are presented along with alternative approaches. Advantages and disadvantages of each method are discussed. Data from a recent comparison of ’ standard-of-care’ triple combination regimens are used for illustration.

This is a preview of subscription content, access via your institution.


  1. 1.

    Myers WR. Handling missing data in clinical trials: An overview. Drug Inf J. 2000;34:525–533.

    Article  Google Scholar 

  2. 2.

    Little RJA, Rubin DB. Statistical Analysis with Missing Data, New York, NY: Wiley; 1987.

    Google Scholar 

  3. 3.

    Murray J. Guidance for Industry—Clinical Considerations for Accelerated and Traditional Approval for Antiviral Drugs Using Plasma Viral RNA Measurements. Rockville, MD: US Food and Drug Administration; 1999.

    Google Scholar 

  4. 4.

    Committee for Proprietary Medicinal Products (CPMP). Points to consider in the Assessment of an Anti-HIV Medicinal Product. London, UK: The European Agency for the Evaluation of Medicinal Products; 2000.

    Google Scholar 

  5. 5.

    Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.

    CAS  Article  Google Scholar 

  6. 6.

    Littell RC, Milliken GA, Stroup WW, Wolfinger RD. SAS System for Mixed Models. Cary, NC: SAS Institute Inc; 1996.

    Google Scholar 

  7. 7.

    Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.

    Article  Google Scholar 

  8. 8.

    Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: Wiley; 1987.

    Google Scholar 

  9. 9.

    Rubin DB. Multiple imputation after 18+ years (with discussion). J Am Stat Assoc. 1996;91:473–489.

    Article  Google Scholar 

  10. 10.

    Schafer JL. Analysis of Incomplete Multivariate Data. New York, NY: Wiley; 1997.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Thomas Kelleher PhD.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kelleher, T., Thiry, A., Wilber, R. et al. Missing Data Methods in HIV Clinical Trials: Regulatory Guidance And Alternative Approaches. Ther Innov Regul Sci 35, 1363–1371 (2001). https://doi.org/10.1177/009286150103500432

Download citation

Key Words

  • Missing data
  • HIV
  • Clinical trials
  • Regulatory guidance