Volume 27, Issue 1 - Vaccines and Immunotherapies
The perils of the unknown: Missing data in clinical studies
Authors: Thomas M. Schindler, Helen Bridge
Abstract
The phenomenon of missing data is ubiquitous in clinical studies. Both the extent of missing data and the structure of missing data can introduce bias into study results and lead to wrong conclusions. Medical writers should be aware of the extent of missing data and should describe the methods used to deal with the issue. This article outlines some of the most commonly used statistical methods for handling missing data. The traditionally used last-observation-carried-forward (LOCF) method to fill data gaps is problematic in many ways. It is better to employ a method that reduces bias, such as multiple imputation (MI) or mixed-effects models for repeated measures (MMRM). Clinical study design can also help minimise the quantity of missing data.
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References
- Committee for Medicinal Products for Human Use (CHMP). 2010. Guideline on missing data in confirmatory clinical trials. EMA/ CPMP/EWP/1776/99 Rev. 1.
- Kenward MG. The handling of missing data in clinical trials. Clin Invest. 2013;3(3): 241–50.
- Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367(14):1355–60.
- Mallinckrodt CH, Sanger TM, Dubé S, DeBrota DJ, Molenberghs G, Carroll RJ, et al. Assessing and interpreting treatment effects in longitudinal clinical trials with missing data. Biol Psychiatry. 2003; 53:754–60.
- National Research Council. 2010. The prevention and treatment of missing data in clinical trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
- O’Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther. 2012;9,3:550–4.
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