The Analysis of Multiple Endpoints in Clinical Trials
Abstract
Treatment comparisons in randomized clinical trials usually involve several endpoints such that conventional significance testing can seriously inflate the overall Type I error rate. One option is to select a single primary endpoint for formal statistical inference, but this is not always feasible. Another approach is to apply Bonferroni correction (i.e., multiply each P-value by the total number of endpoints). Its conservatism for correlated endpoints is examined for multivariate normal data. A third approach is to derive an appropriate global test statistic and this paper explores one such test applicable to any set of asymptotically normal test statistics. Quantitative, binary, and survival endpoints are all considered within this general framework. Two examples are presented and the relative merits of the proposed strategies are discussed.
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The global statistic's power is equal or greater than that of any of the single dichotomous outcomes.
“The global statistic (Wald test), as derived from a general linear model with logit-link function, simultaneously tests for effect in multiple dichotomous outcomes; as such, the power of the global test is equal or greater than that of any of the single dichotomous outcomes. 35”
From Statistical Analysis of the Primary Outcome in Acute Stroke Trials (2012) · cites this paper as [35]
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References 4 · Cited by 524
- Authors
- Pocock, S. J., Geller, N. L., & Tsiatis, A. A.
- Affiliations
- University of London (GB)
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1987-09
- Volume
- 43
- Issue
- 3
- Pages
- 487
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2071381562
- Cited by
- 524
- References
- 4
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 11 · 2023: 2 · 2022: 12 · 2021: 7 · 2020: 5 · 2019: 11 · 2018: 11 · 2017: 10 · 2016: 15 · 2015: 19 · 2014: 31 · 2013: 15 · 2012: 16
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Cancer Society
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- multiple endpoints, type I error inflation, Bonferroni correction, multivariate normal data, primary endpoint selection, survival endpoints, binary endpoints, randomized controlled trials
- MeSH terms
- Asthma, Biometry, Clinical Trials as Topic, Double-Blind Method, Humans, Lung Diseases, Obstructive, Lung Volume Measurements, Placebos, Random Allocation
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