Literature DB >> 11386629

Bayesian assessment of sample size for clinical trials of cost-effectiveness.

A O'Hagan1, J W Stevens.   

Abstract

The authors present an analysis of the choice of sample sizes for demonstrating cost-effectiveness of a new treatment or procedure, when data on both cost and efficacy will be collected in a clinical trial. The Bayesian approach to statistics is employed, as well as a novel Bayesian criterion that provides insight into the sample size problem and offers a very flexible formulation.

Mesh：

Year:  2001        PMID： 11386629     DOI： 10.1177/0272989X0102100307

Source DB:  PubMed          Journal:  Med Decis Making        ISSN： 0272-989X            Impact factor:   2.583


  19 in total

Review 1.  Sample size determination for cost-effectiveness trials.

Authors:  Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2011-11       Impact factor: 4.981

2.  Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results.

Authors:  Theodoros Mantopoulos; Paul M Mitchell; Nicky J Welton; Richard McManus; Lazaros Andronis
Journal:  Eur J Health Econ       Date:  2015-10-07

Review 3.  Role of pharmacoeconomic analysis in R&D decision making: when, where, how?

Authors:  Paul Miller
Journal:  Pharmacoeconomics       Date:  2005       Impact factor: 4.981

4.  Sample size and power for cost-effectiveness analysis (part 1).

Authors:  Henry A Glick
Journal:  Pharmacoeconomics       Date:  2011-03       Impact factor: 4.981

5.  Bayes factor design analysis: Planning for compelling evidence.

Authors:  Felix D Schönbrodt; Eric-Jan Wagenmakers
Journal:  Psychon Bull Rev       Date:  2018-02

6.  Bayesian clinical trial design using historical data that inform the treatment effect.

Authors:  Matthew A Psioda; Joseph G Ibrahim
Journal:  Biostatistics       Date:  2019-07-01       Impact factor: 5.899

7.  Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.

Authors:  Matthew A Psioda; Kuolung Hu; Yang Zhang; Jean Pan; Joseph G Ibrahim
Journal:  Biometrics       Date:  2019-11-11       Impact factor: 2.571

8.  Evaluating the impact of a community health worker programme on non-communicable disease, malnutrition, tuberculosis, family planning and antenatal care in Neno, Malawi: protocol for a stepped-wedge, cluster randomised controlled trial.

Authors:  Elizabeth L Dunbar; Emily B Wroe; Basimenye Nhlema; Chiyembekezo Kachimanga; Ravi Gupta; Celia Taylor; Annie Michaelis; Katie Cundale; Luckson Dullie; Arnold Jumbe; Lawrence Nazimera; Ryan McBain; Richard J Lilford; Samuel Ian Watson
Journal:  BMJ Open       Date:  2018-07-13       Impact factor: 2.692

9.  Adaptive Multivariate Global Testing.

Authors:  Giorgos Minas; John A D Aston; Nigel Stallard
Journal:  J Am Stat Assoc       Date:  2014-06-13       Impact factor: 5.033

10.  A decision-theoretic approach to Bayesian clinical trial design and evaluation of robustness to prior-data conflict.

Authors:  Silvia Calderazzo; Manuel Wiesenfarth; Annette Kopp-Schneider
Journal:  Biostatistics       Date:  2022-01-13       Impact factor: 5.279
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