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

Chunyan Cai, Jing Ning, Xuelin Huang

Research output: Contribution to journalArticle

  • 1 Citations

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.

LanguageEnglish
Pages1219-1229
Number of pages11
JournalStatistical Methods in Medical Research
Volume27
Issue number4
DOIs
StatePublished - Apr 1 2018

Fingerprint

Cost-effectiveness
Stroke
Cost-Benefit Analysis
Animals
Adaptive Design
Research
Dose
Appointments and Schedules
Clinical Trials
Simulation Study
Shrinkage
Optimal Strategy
Assign
Efficacy
Schedule
Resources
Design
Evaluate
Therapeutics
Strategy

Keywords

  • Admissible set
  • animal study
  • Bayesian approach
  • cost-effectiveness design
  • multi-stage design

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research. / Cai, Chunyan; Ning, Jing; Huang, Xuelin.

In: Statistical Methods in Medical Research, Vol. 27, No. 4, 01.04.2018, p. 1219-1229.

Research output: Contribution to journalArticle

Cai, Chunyan ; Ning, Jing ; Huang, Xuelin. / A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research. In: Statistical Methods in Medical Research. 2018 ; Vol. 27, No. 4. pp. 1219-1229.
@article{a742f68ec9f448e5bb2400975995d98c,
title = "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.",
keywords = "Admissible set, animal study, Bayesian approach, cost-effectiveness design, multi-stage design",
author = "Chunyan Cai and Jing Ning and Xuelin Huang",
year = "2018",
month = "4",
day = "1",
doi = "10.1177/0962280216657853",
language = "English",
volume = "27",
pages = "1219--1229",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "4",

}

TY - JOUR

T1 - A Bayesian multi-stage cost-effectiveness design for animal studies in stroke research

AU - Cai, Chunyan

AU - Ning, Jing

AU - Huang, Xuelin

PY - 2018/4/1

Y1 - 2018/4/1

N2 - 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.

AB - 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.

KW - Admissible set

KW - animal study

KW - Bayesian approach

KW - cost-effectiveness design

KW - multi-stage design

UR - http://www.scopus.com/inward/record.url?scp=85042866061&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042866061&partnerID=8YFLogxK

U2 - 10.1177/0962280216657853

DO - 10.1177/0962280216657853

M3 - Article

VL - 27

SP - 1219

EP - 1229

JO - Statistical Methods in Medical Research

T2 - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 4

ER -