DeepAI AI Chat
Log In Sign Up

Equivalence tests for binary efficacy-toxicity responses

by   Holger Dette, et al.

Clinical trials often aim to compare a new drug with a reference treatment in terms of efficacy and/or toxicity depending on covariates such as, for example, the dose level of the drug. Equivalence of these treatments can be claimed if the difference in average outcome is below a certain threshold over the covariate range. In this paper we assume that the efficacy and toxicity of the treatments are measured as binary outcome variables and we address two problems. First, we develop a new test procedure for the assessment of equivalence of two treatments over the entire covariate range for a single binary endpoint. Our approach is based on a parametric bootstrap, which generates data under the constraint that the distance between the curves is equal to the pre-specified equivalence threshold. Second, we address equivalence for bivariate binary (correlated) outcomes by extending the previous approach for a univariate response. For this purpose we use a 2-dimensional Gumbel model for binary efficacy-toxicity responses. We investigate the operating characteristics of the proposed approaches by means of a simulation study and present a case study as an illustration.


page 1

page 2

page 3

page 4


Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates

In sequential experiments, subjects become available for the study over ...

Equivalence of regression curves sharing common parameters

In clinical trials the comparison of two different populations is a freq...

Comparing regression curves – an L^1-point of view

In this paper we compare two regression curves by measuring their differ...

Assessing Biosimilarity using FunctionalMetrics

In recent years there have been a lot of interest to test for similarity...

Assessing Biosimilarity using Functional Metrics

In recent years there have been a lot of interest to test for similarity...