Mixed effects historical varying-coefficient model for evaluating dose–response in flexible dose trials

Toshihiro Misumi and Sadanori Konishi

Journal of the Royal Statistical Society Series C, 2016, vol. 65, issue 2, 331-344

Abstract: type="main" xml:id="rssc12120-abs-0001">

Varying-coefficient models provide a useful tool to explore the dynamic pattern in various fields of science, such as epidemiology, medical research and ecology. Crucial issues arise in assessing the dose–response relationship from flexible dose clinical trial data: the current response is affected by not only the current dose level but also past dose levels, i.e. there is a time lag in the effectiveness of treatment and, also, there is considerable variability between subjects. To address these issues, we propose a novel non-linear varying-coefficient model, called a mixed effects historical varying-coefficient model (MEHVCM), for estimating dose–response curves in longitudinal flexible dose trials. This model enables us to describe historical effectiveness curves and subject-specific curves. Unknown parameters included in the MEHVCM are estimated by the maximum penalized likelihood method along with the EM algorithm. Monte Carlo experiments are conducted to investigate the performance of the MEHVCM for evaluating dose–response relationships in flexible dose trials. We apply the proposed model to the analysis of data from a multiple-sclerosis clinical trial.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1111/rssc.2016.65.issue-2 (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:bla:jorssc:v:65:y:2016:i:2:p:331-344

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().