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Exact t and F Tests for Analyzing Studies with Multiple Endpoints
Abstract
SUMMARY The paper describes some methods of forming sum aggregates in multivariate analysis. These methods are useful for the statistical analysis of investigations with multiple endpoints. In particular, the sum statistics can be applied if differences between two or more populations have to be tested and a factorial structure of the means and covariances can be supposed. For this approach the calculation of principal components has great importance. The central mathematical problem is to construct statistics which permit exact t and F tests, i.e., tests keeping the error of first kind at the prescribed level. The paper is based on the theory of spherical matrix distributions treated extensively in the book by Fang and Zhang (1990, Generalized Multivariate Analysis). This paper refers to the problem of the analysis of high-dimensional observations which often occurs in clinical studies. We start from the proposal of O'Brien (1984) to use sum aggregates of the form P E Xi ~~~~~~~~~~~(1.1) i= i
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Cited by
A well-conditioned estimator for large-dimensional covariance matrices
Ledoit, O., & Wolf, M. (2004) · Journal of Multivariate Analysis · doi:10.1016/s0047-259x(03)00096-4 · Cited by 3,046
Design and Analysis of Quality of Life Studies in Clinical Trials
Fairclough, D. L. (2002) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420035438 · Cited by 461
A general modular framework for gene set enrichment analysis
Ackermann, M., & Strimmer, K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-47 · Cited by 358
Metadata
- Authors
- Lauter, J.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1996-09
- Volume
- 52
- Issue
- 3
- Pages
- 964
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2331897191
- Cited by
- 175
- References
- 0
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lauter, J. (1996). Exact t and F Tests for Analyzing Studies with Multiple Endpoints. Biometrics. https://doi.org/10.2307/2533057
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
