Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research

Small Sample Properties of Multiple Marginal Models and a Resampling Approach

authored by: Sören Budig, Klaus Jung, Mario Hasler, Frank Schaarschmidt
Abstract: In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step (Formula presented.) -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.
Organisation(s): Department of Biostatistics
External Organisation(s): University of Veterinary Medicine of Hannover, Foundation
Kiel University
Type: Article
Journal: Biometrical journal
Volume: 66
No. of pages: 15
ISSN: 0323-3847
Publication date: 02.07.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Statistics and Probability, Statistics, Probability and Uncertainty
Electronic version(s): https://doi.org/10.1002/bimj.202300197 (Access: Open)

BibTeX

@article{d8baf0f2c50c447e82a5afbbfb4aa6b3,
title = "Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach",
abstract = "In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step (Formula presented.) -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.",
keywords = "bootstrap, family-wise error rate, generalized linear models, multiple comparisons, power",
author = "S{\"o}ren Budig and Klaus Jung and Mario Hasler and Frank Schaarschmidt",
note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Biometrical Journal published by Wiley-VCH GmbH.",
year = "2024",
month = jul,
day = "2",
doi = "10.1002/bimj.202300197",
language = "English",
volume = "66",
journal = "Biometrical journal",
issn = "0323-3847",
publisher = "Wiley-VCH Verlag",
number = "5",
}