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Analysis of opioid consumption in clinical trials: a simulation based analysis of power of four approaches
Univ Copenhagen, Dept Drug Design & Pharmacol, Jagtvej 160, Copenhagen, Denmark..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Univ Copenhagen, Dept Drug Design & Pharmacol, Jagtvej 160, Copenhagen, Denmark..
Univ Copenhagen, Dept Drug Design & Pharmacol, Jagtvej 160, Copenhagen, Denmark..
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2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 4, 325-333 p.Article in journal (Refereed) Published
Abstract [en]

Inconsistent trial design and analysis is a key reason that few advances in postoperative pain management have been made from clinical trials analyzing opioid consumption data. This study aimed to compare four different approaches to analyze opioid consumption data. A repeated time-to-event (RTTE) model in NONMEM was used to simulate clinical trials of morphine consumption with and without a hypothetical adjuvant analgesic in doses equivalent to 15-62% reduction in morphine consumption. Trials were simulated with duration of 24-96 h. Monte Carlo simulation and re-estimation were performed to determine sample size required to demonstrate efficacy with 80% power using t test, Mann-Whitney rank sum test, time-to-event (TTE) modeling and RTTE modeling. Precision of efficacy estimates for RTTE models were evaluated in 500 simulations. A sample size of 50 patients was required to detect 37% morphine sparing effect with at least 80% power in a 24 h trial with RTTE modeling whereas the required sample size was 200 for Mann-Whitney, 180 for t-test and 76 for TTE models. Extending the trial duration from 24 to 96 h reduced the required sample size by 3.1 fold with RTTE modeling. Precise estimate of potency was obtained with a RTTE model accounting for both morphine effects and time-varying covariates on opioid consumption. An RTTE analysis approach proved better suited for demonstrating efficacy of opioid sparing analgesics than traditional statistical tests as a lower sample size was required due the ability to account for time-varying factors including PK.

Place, publisher, year, edition, pages
SPRINGER/PLENUM PUBLISHERS , 2017. Vol. 44, no 4, 325-333 p.
Keyword [en]
Postoperative pain, Analgesics, Repeated time-to-event modeling, Clinical trial simulation, Monte Carlo Mapped Power
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-331214DOI: 10.1007/s10928-017-9522-4ISI: 000405835000004PubMedID: 28389762OAI: oai:DiVA.org:uu-331214DiVA: diva2:1150219
Available from: 2017-10-18 Created: 2017-10-18 Last updated: 2017-10-18Bibliographically approved

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Nyberg, JoakimSvensson, Ulrika S H

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