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One-sided significance tests for generalized linear models under dichotomous response.
1Statistics and Biomathematics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709.
Biometrics
|June 1, 1990
Summary
This study evaluates one-sided significance tests for dichotomous response models, often used with logistic regression. Procedures performed well, achieving nominal sizes with around 100 samples.
Area of Science:
- Biostatistics
- Statistical Modeling
- Experimental Design
Background:
- Dichotomous response models are frequently employed in experimental research.
- Generalized linear models, like the logit model, are commonly used when explanatory variables are present.
Purpose of the Study:
- To examine one-sided significance tests for dichotomous response models.
- To evaluate procedures for testing null effects against one-sided alternatives, including Bonferroni-adjusted Wald tests and inequality-constrained likelihood ratio tests.
Main Methods:
- Monte Carlo simulations were used to assess the small-sample properties of various significance testing procedures.
- The study considered extensions for one-sided tests against a control or standard.
Main Results:
- The examined procedures demonstrated good performance in small samples.
- Nominal sizes were generally achieved with total sample sizes approaching 100 experimental units.
Conclusions:
- The evaluated one-sided significance testing methods are effective for dichotomous response models.
- These methods provide reliable results even with moderate sample sizes, supporting their use in experimental settings.