Testing disease progression under the proportional reduction in decline in Alzheimer's disease studies

Zhixin Tang, Aidong A. Ding, Yahui Zhang, Guoqiao Wang and Guogen Shan

Journal of Applied Statistics, 2026, vol. 53, issue 3, 431-446

Abstract: To assess the treatment-placebo difference in Alzheimer's disease (AD) trials, saved time measure provides an easy interpretation of the treatment effect in months as an alternative measure to the treatment-placebo difference at the pre-specified visit that is often estimated from the fitted model. The current method to estimate saved time utilizes the disease progression curve of the placebo group, and this method is primarily descriptive. To fill the gap of the statistical inference for saved time, we propose to develop the likelihood ratio test and the score test under the proportional reduction in decline model. One AD trial data set was utilized to compare the proposed tests and the existing Wald-type test with regard to type I error rate and statistical power. We found that the likelihood ratio test and the score test have similar statistical power, but the score test has better control with regard to type I error rate. The two new tests are more powerful than the Wald test when a new treatment has proportional reduction in decline or constant delay, while the Wald test can have a higher statistical power when a constant treatment effect is expected from a new treatment.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2025.2514153 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:53:y:2026:i:3:p:431-446

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2025.2514153

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().