A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.

Details

Ressource 1Download: 31011658_BIB_DF7E6F696218.pdf (416.20 [Ko])
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_DF7E6F696218
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A statistical approach to identify optimal inclusion criteria: An application to acute stroke clinical trials.
Journal
Contemporary clinical trials communications
Author(s)
Ball R.L., Jiang B., Desai M., Michel P., Eskandari A., Jovin T., Wintermark M.
ISSN
2451-8654 (Electronic)
ISSN-L
2451-8654
Publication state
Published
Issued date
06/2019
Peer-reviewed
Oui
Volume
14
Pages
100355
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
To develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria.
We developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment course, and clinical outcome among patients that may have been included/excluded in the trials. We applied this approach to the study population from four recent positive acute stroke clinical trials: MR CLEAN, EXTEND-IA, ESCAPE, and SWIFT PRIME, applying published trial criteria to an independent registry of 612 acute stroke patients, since we did not have access to the complete trial data.
Although reported NNT were similar for EXTEND-IA, SWIFT PRIME and ESCAPE, and somewhat higher for MR CLEAN, NNS varied across the trials from 21 for EXTEND-IA, 27 for MR CLEAN, to 46 for ESCAPE and 64 for SWIFT PRIME, reflecting less and more stringent inclusion criteria, respectively. Although there were significant differences in imaging biomarkers and other clinical characteristics among patients that may have been included/excluded in the trials, these differences did not translate to significant differences in treatment course or clinical outcomes.
Our study proposes a robust statistical approach that can be applied to a larger pooled trial dataset, if made available, to objectively compare across clinical trials and inform inclusion criteria of future trials. Pooled analysis of the acute stroke trial data is needed to determine which imaging biomarker inclusion criteria are critical and which may be relaxed. If this procedure were applied across the pooled trial data, it could decrease costs and refine the design of future trials to be the most efficacious for the greatest number of patients.
Keywords
Acute stroke, Clinical trial, Inclusion criteria, Patient selection
Pubmed
Open Access
Yes
Create date
03/05/2019 16:29
Last modification date
20/08/2019 16:03
Usage data