Consequences of dichotomization
Abstract
Dichotomization is the transformation of a continuous outcome (response) to a binary outcome. This approach, while somewhat common, is harmful from the viewpoint of statistical estimation and hypothesis testing. We show that this leads to loss of information, which can be large. For normally distributed data, this loss in terms of Fisher's information is at least 1 − 2/π (or 36%). In other words, 100 continuous observations are statistically equivalent to 158 dichotomized observations. The amount of information lost depends greatly on the prior choice of cut points, with the optimal cut point depending upon the unknown parameters. The loss of information leads to loss of power or conversely a sample size increase to maintain power. Only in certain cases, for instance, in estimating a value of the cumulative distribution function and when the assumed model is very different from the true model, can the use of dichotomized outcomes be considered a reasonable approach. Copyright © 2008 John Wiley & Sons, Ltd.
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Categorising a continuous outcome leads to loss of information.
“Categorising a continuous outcome leads to loss of information.38 39 40”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [39]
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- Authors
- Fedorov, V., Mannino, F., & Zhang, R.
- Affiliations
- GlaxoSmithKline (United States) (US)
- Corresponding author
- Mannino, Frank
- ORCID
- Mannino, Frank
- Venue
- Pharmaceutical Statistics
- Publisher
- Wiley
- Published
- 2008-04-03
- Volume
- 8
- Issue
- 1
- Pages
- 50-61
- ISSN
- 1539-1604 (ISSN-L)
- 1539-1612
- OpenAlex
- W2139425275
- Cited by
- 303
- References
- 22
- Citations by year
- 2026: 21 · 2025: 22 · 2024: 30 · 2023: 24 · 2022: 29 · 2021: 18 · 2020: 20 · 2019: 19 · 2018: 13 · 2017: 14 · 2016: 13 · 2015: 11 · 2014: 14 · 2013: 14 · 2012: 11
- FWCI
- 8.52
- 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 › Statistical Methods in Epidemiology
- Keywords
- dichotomization, information loss, Fisher information, cut-point selection, statistical power, sample size, hypothesis testing, normally distributed data, cumulative distribution function estimation
- MeSH terms
- Data Interpretation, Statistical, Humans, Research Design, Time Factors, Models, Statistical, Normal Distribution, Treatment Outcome
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