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A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research.

ABSTRACT: Much progress has been made in the area of adaptive designs for clinical trials. However, little has been done regarding adaptive designs to identify optimal treatment strategies in animal studies. Motivated by an animal study of a novel strategy for treating strokes, we propose a Bayesian multi-stage cost-effectiveness design to simultaneously identify the optimal dose and determine the therapeutic treatment window for administrating the experimental agent. We consider a non-monotonic pattern for the dose-schedule-efficacy relationship and develop an adaptive shrinkage algorithm to assign more cohorts to admissible strategies. We conduct simulation studies to evaluate the performance of the proposed design by comparing it with two standard designs. These simulation studies show that the proposed design yields a significantly higher probability of selecting the optimal strategy, while it is generally more efficient and practical in terms of resource usage.

SUBMITTER: Cai C

PROVIDER: S-EPMC5364074 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research.

Cai Chunyan C Ning Jing J Huang Xuelin X

Statistical methods in medical research 20160708 4

Much progress has been made in the area of adaptive designs for clinical trials. However, little has been done regarding adaptive designs to identify optimal treatment strategies in animal studies. Motivated by an animal study of a novel strategy for treating strokes, we propose a Bayesian multi-stage cost-effectiveness design to simultaneously identify the optimal dose and determine the therapeutic treatment window for administrating the experimental agent. We consider a non-monotonic pattern f ...[more]

PMID: 27405325

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Project description:BackgroundMulti-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure.MethodsWe redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds.ResultsOur findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3).ConclusionAdaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates.Trial registrationClinicalTrials.gov Identifier: NCT01342692 .

Project description:BackgroundDespite progress in stroke therapy (e.g., revascularisation interventions by thrombolysis and/or thrombectomy, organised stroke care), many stroke survivors will have impairment of neurological function. We aimed to compare the cost-effectiveness of an oral natural formulation, MLC601, versus placebo in functional recovery among subjects receiving standard of care after an ischemic stroke of intermediate severity assessed with NIH Stroke Scale at baseline (b-NIHSS 8-14).MethodsA Markov cohort model with a 2-year time horizon was developed to simulate patients from a published randomised placebo-controlled clinical trial of MLC601 in their post-stroke functional recovery assessed by modified Rankin Score (mRS), from a health system perspective. Transition probabilities were derived from a multi-centre clinical trial in South East Asia. As cost and utility data were not collected in the trial, therefore we extracted them from the published literature. The main outcomes were incremental cost, incremental quality-adjusted life-year (QALY) gained, and incremental cost-effectiveness ratio (ICER). Besides base-case and sensitivity analyses, we performed subgroup analyses to explore the heterogeneity of patients with poor-prognosis factors (b-NIHSS 10-14, stroke onset to treatment time > 48 h, rehabilitation during first 3 month). All costs are expressed in 2022 Euro and USD, with an annual discount rate of 3% applied to costs and QALYs.ResultsBase-case analysis showed that MLC601 was cost-effective compared with placebo, with €5,080 saved and 0.45 QALY gained, resulting in an ICER of -€11,352.50 per QALY gained. Similarly, results from subgroup analyses indicated that the use of MLC601 was a dominant strategy in all subgroups with poor-prognosis factors. Sensitivity analyses revealed the results were robust.ConclusionCompared with placebo on top of standard stroke care, MLC601 was cost-effective in post-stroke functional recovery over two years. Due to the lack of cost and utility data from the study population, the results might not be generalizable to other settings. Further studies with country-specific data are needed to confirm the results of this study.Trial registrationURL http://www.Clinicaltrialsgov . Unique identifier NCT00554723 November 7, 2007.

Dataset Information

A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research.

Publications

A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research.

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