Literature DB >> 16316789

Enriched analgesic efficacy studies: an assessment by clinical trial simulation.

Hendrikus J M Lemmens1, D Russell Wada, Catherine Munera, Ahmed Eltahtawy, Donald R Stanski.   

Abstract

BACKGROUND AND
OBJECTIVE: Enrichment strategies which select subjects who appear to respond to the drug have been used in drug studies to demonstrate clinical efficacy. We have used clinical trial simulation techniques to examine factors that are relevant in clinical trial design based on enrichment where poor responders are excluded from the double-blind phase of the study.
METHODS: Simulations were performed for an analgesic trial design involving an open-dose titration phase (enrichment phase) followed by a double-blind, randomized, placebo-controlled maintenance phase. Enrichment was examined by excluding subjects above a predefined pain score (cutoff) from analysis of efficacy for the maintenance phase. Cutoff pain scores ranging from 4 to 7 on a 0 to 10 categorical scale were examined. A database consisting of chronic pain patients who participated in studies with a new formulation of buprenorphine was used to build the simulation model. Since no data were available for the key model variable "correlation between treatment and placebo response", values of 0.25, 0.5, and 0.75 were used for the simulations.
RESULTS: A correlation between treatment and placebo effect ranging from 0.75 to 0.25 will cause the likelihood of trial success to vary from 50% to 95%. This model also shows that recruitment efficiency will decrease with the use of lower cutoff pain scores.
CONCLUSION: Prior to using enrichment techniques, investigators must consider the correlation between treatment effect and placebo response to optimize clinical trial design.

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Year:  2005        PMID: 16316789     DOI: 10.1016/j.cct.2005.10.005

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.226


  4 in total

Review 1.  Comparison of effect sizes between enriched and nonenriched trials of analgesics for chronic musculoskeletal pain: a systematic review.

Authors:  Tie P Yamato; Chris G Maher; Bruno T Saragiotto; Christina Abdel Shaheed; Anne M Moseley; Chung-Wei Christine Lin; Bart Koes; Andrew J McLachlan
Journal:  Br J Clin Pharmacol       Date:  2017-08-11       Impact factor: 4.335

2.  Simulation of correlated continuous and categorical variables using a single multivariate distribution.

Authors:  Stacey J Tannenbaum; Nicholas H G Holford; Howard Lee; Carl C Peck; Diane R Mould
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-10-12       Impact factor: 2.745

Review 3.  Translational PK-PD modeling in pain.

Authors:  Ashraf Yassen; Paul Passier; Yasuhisa Furuichi; Albert Dahan
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-11-30       Impact factor: 2.745

4.  Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.

Authors:  D Teutonico; F Musuamba; H J Maas; A Facius; S Yang; M Danhof; O Della Pasqua
Journal:  Pharm Res       Date:  2015-05-21       Impact factor: 4.200

  4 in total

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