Work

Analysis of Covariance in General Factorial Designs Through Multiple Contrast Tests Under Variance Heteroscedasticity

Year: 2025

Type: article

Abstract: ABSTRACT A common goal in clinical trials is to conduct tests on estimated treatment effects adjusted for covariates such as age or sex. Analysis of Covariance (ANCOVA) is often used in these scenarios to test the global null hypothesis of no treatment effect using an ‐test. However, in several samples, the ‐test does not provide any information about individual null hypotheses and has strict assumptions such as variance homoscedasticity. We extend the method proposed by Konietschke et al. [“Analysis of Covariance Under Variance Heteroscedasticity in General Factorial Designs,” Statistics in Medicine 40 (2021): 4732–4749] to a multiple contrast test procedure (MCTP), which allows us to test arbitrary linear hypotheses and provides information about the global‐ as well as the individual null hypotheses. Further, we can calculate compatible simultaneous confidence intervals for the individual effects. We derive a small sample size approximation of the distribution of the test statistic via a multivariate t‐distribution. As an alternative, we introduce a Wild‐bootstrap method. Extensive simulations show that our methods are applicable even when sample sizes are small. Their application is further illustrated within a real data example.

Source: Statistics in Medicine

DOI: 10.1002/sim.70018

Authors: Matthias Becher, Ludwig A. Hothorn, Frank Konietschke

Institutions: Charité - Universitätsmedizin Berlin, Leibniz University Hannover

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,450

Funder: Deutsche Forschungsgemeinschaft