Topical Area: Methods and Protocols
Objectives : In clinical trials for dietary supplements and functional foods, the study population tends to be a mixture of healthy subjects and those who are not so healthy but are not definitely diseased (called "borderline subjects"). For such heterogeneous populations, the t-test and ANCOVA method often fail to provide the desired treatment efficacy. We propose an alternative approach for the efficacy evaluation of dietary supplements and functional foods based on a change-point linear regression model.
Methods : Suppose we are interested in analyzing data from a randomized controlled clinical trial of dietary supplements which was conducted under what is called pre-post design. We propose the change-point regression model (CPRM) as new analysis method. The model does not require the assumption of a constant treatment effect and provides clinically interpretable results. By employing the AIC-based profile likelihood method, inferences can be made easily using standard statistical software.
CPRM method was applied to (-)-hydroxycitric acid (HCA) study data, and the merit of the method was demonstrated by comparing it with traditional methods those are ANCOVA or t-test. The AICs of CPRM, ANCOVA and t-test were 667.2, 685.9 and 685.0 respectively. Therefore, CPRM is well fit than traditional methods. Advantage of CPRM is that it can simultaneously provide information on effect appearance points and we succeeded estimation of the point in (-)-HCA study.
Conclusions : We propose an alternative approach for the efficacy evaluation of dietary supplements and functional foods based on a CPRM. By employing the AIC-based profile likelihood method, inferences can be made easily using standard statistical software. The proposed method was applied to the Garcinia study data, and the merit of the method was demonstrated by comparing it with the ANCOVA models.
Funding Sources : This research did not received any specific grant from funding agencies in the public. commercial, or not for-profit sectors.
Dep. Pharmacy, Yokohama University of Pharmacy
Div. Medical Informatics, National Hospital Organization Shikoku Cancer Center
Biomedical Statistics, Graduate School of Medicine, Osaka University
Biostatistics Center, Kurume University