Changepoint Statistics for Assessing a Treatment-Covariate Interaction

Koziol, James A.; Wu, Shu-Chen

doi:10.2307/2533076

Cited by 8 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of education, TCI is known as aptitude treatment interaction (Cronbach & Snow, 1977;Snow, 1991) or attribute treatment interaction. We prefer the term TCI, which is used in medical research (e.g., Koziol & Wu, 1996), to refer to the more general situation of an interaction between t and x.…”

Section: Introductionmentioning

confidence: 99%

The regression trunk approach to discover treatment covariate interaction

Dusseldorp

Meulman

2004

Psychometrika

View full text Add to dashboard Cite

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable with multiple covariates. The performance of RTA is compared to the classical method of forward stepwise regression. The results of two simulation studies, in which the true interactions axe modeled as threshold interactions, show that RTA detects the interactions in a higher number of cases (82.3% in the first simulation study, and 52.3% in the second) than stepwise regression (56.5% and 20.5%). In a real data example the final RTA model has a higher cross-validated variance-accounted-for (29.8%) than the stepwise regression model (12.5%). All of these results indicate that RTA is a promising alternative method for demonstrating differential effectiveness of treatments.

show abstract

Section: Introductionmentioning

confidence: 99%

The regression trunk approach to discover treatment covariate interaction

Dusseldorp

Meulman

2004

Psychometrika

View full text Add to dashboard Cite

show abstract

“…If the covariate of interest is continuous, then its range may be split into two parts, and di erences in treatment e ect examined between the two subsets. Koziol and Wu [11] propose a method to determine a cut-o point for dividing the covariate axis into the two categories for assessing the treatment-covariate interaction.…”

Section: Introductionmentioning

confidence: 99%

A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data

2000

View full text Add to dashboard Cite

“…One challenge in hypothesis testing for change point models is that under the null hypothesis, the threshold parameter becomes unidentifiable. This type of problem, highlighted by Davies , has motivated much previous works in the biostatistical literature on change point testing . However, some methods, for example , do not apply to the model we study in this paper, which is discontinuous in the change point variable.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 3, we propose a maximum of weighted scores test and a maximum of likelihood ratios test for a change point model with interaction term. Of particular relevance to Section 3 is the work by Koziol and Wu , which was concerned with identifying a change point in a predictor that modifies the effect of treatment in a randomized two‐arm clinical trial. In addition, Pastor‐Barriuso, Guallar and Coresh (2003) proposed a general, maximum of likelihood ratios test for change point models that can also be applied with an interaction term.…”

Section: Introductionmentioning

confidence: 99%

Change point testing in logistic regression models with interaction term

Fong

Permar³

2015

Statist. Med.

View full text Add to dashboard Cite

A threshold effect takes place in situations where the relationship between an outcome variable and a predictor variable changes as the predictor value crosses a certain threshold/change point. Threshold effects are often plausible in a complex biological system, especially in defining immune responses that are protective against infections such as HIV-1, which motivates the current work. We study two hypothesis testing problems in change point models. We first compare three different approaches to obtaining a p-value for the maximum of scores test in a logistic regression model with change point variable as a main effect. Next, we study the testing problem in a logistic regression model with the change point variable both as a main effect and as part of an interaction term. We propose a test based on the maximum of likelihood ratios test statistic and obtain its reference distribution through a Monte Carlo method. We also propose a maximum of weighted scores test that can be more powerful than the maximum of likelihood ratios test when we know the direction of the interaction effect. In simulation studies, we show that the proposed tests have correct type I error and higher power than several existing methods. We illustrate the application of change point model-based testing methods in a recent study of immune responses that are associated with the risk of mother to child transmission (MTCT) of HIV-1.

show abstract

Changepoint Statistics for Assessing a Treatment-Covariate Interaction

Cited by 8 publications

References 12 publications

The regression trunk approach to discover treatment covariate interaction

The regression trunk approach to discover treatment covariate interaction

A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data

Change point testing in logistic regression models with interaction term

Contact Info

Product

Resources

About