Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models
Abstract
The need to use rigorous, transparent, clearly interpretable, and scientifically justified methodology for preventing and dealing with missing data in clinical trials has been a focus of much attention from regulators, practitioners, and academicians over the past years. New guidelines and recommendations emphasize the importance of minimizing the amount of missing data and carefully selecting primary analysis methods on the basis of assumptions regarding the missingness mechanism suitable for the study at hand, as well as the need to stress‐test the results of the primary analysis under different sets of assumptions through a range of sensitivity analyses. Some methods that could be effectively used for dealing with missing data have not yet gained widespread usage, partly because of their underlying complexity and partly because of lack of relatively easy approaches to their implementation. In this paper, we explore several strategies for missing data on the basis of pattern mixture models that embody clear and realistic clinical assumptions. Pattern mixture models provide a statistically reasonable yet transparent framework for translating clinical assumptions into statistical analyses. Implementation details for some specific strategies are provided in an Appendix (available online as Supporting Information), whereas the general principles of the approach discussed in this paper can be used to implement various other analyses with different sets of assumptions regarding missing data. Copyright © 2013 John Wiley & Sons, Ltd.
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- Authors
- Ratitch, B., O'Kelly, M., & Tosiello, R.
- Affiliations
- IQVIA (United States) (US)
- Quintiles Dublin Ireland
- Sunovion (United States) (US)
- Corresponding author
- Ratitch, Bohdana
- Venue
- Pharmaceutical Statistics
- Publisher
- Wiley
- Published
- 2013-01-04
- Volume
- 12
- Issue
- 6
- Pages
- 337-347
- ISSN
- 1539-1604 (ISSN-L)
- 1539-1612
- OpenAlex
- W1977634030
- Cited by
- 155
- References
- 23
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 11 · 2023: 10 · 2022: 14 · 2021: 11 · 2020: 20 · 2019: 15 · 2018: 10 · 2017: 10 · 2016: 11 · 2015: 12 · 2014: 9 · 2013: 5
- FWCI
- 10.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pharmaceutical Statistics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- missing data, clinical trials, pattern-mixture models, missing data mechanisms, sensitivity analysis
- MeSH terms
- Clinical Trials as Topic, Data Collection, Data Interpretation, Statistical, Humans, Research Design, Models, Statistical, Guidelines as Topic
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