Literature DB >> 17640107

The contrast and convergence of Bayesian and frequentist statistical approaches in pharmacoeconomic analysis.

Grant H Skrepnek1.   

Abstract

The application of Bayesian statistical analyses has been facilitated in recent years by methodological advances and an increasing complexity necessitated within research. Substantial debate has historically accompanied this analytic approach relative to the frequentist method, which is the predominant statistical ideology employed in clinical studies. While the essence of the debate between the two branches of statistics centres on differences in the use of prior information and the definition of probability, the ramifications involve the breadth of research design, analysis and interpretation. The purpose of this paper is to discuss the application of frequentist and Bayesian statistics in the pharmacoeconomic assessment of healthcare technology. A description of both paradigms is offered in the context of potential advantages and disadvantages, and applications within pharmacoeconomics are briefly addressed. Additional considerations are presented to stimulate further development and to direct appropriate applications of each method such that the integrity and robustness of scientific inference be strengthened.

Mesh:

Year:  2007        PMID: 17640107     DOI: 10.2165/00019053-200725080-00003

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  69 in total

Review 1.  Sifting the evidence-what's wrong with significance tests?

Authors:  J A Sterne; G Davey Smith
Journal:  BMJ       Date:  2001-01-27

Review 2.  Methods in health service research. An introduction to bayesian methods in health technology assessment.

Authors:  D J Spiegelhalter; J P Myles; D R Jones; K R Abrams
Journal:  BMJ       Date:  1999-08-21

3.  Who's afraid of Thomas Bayes?

Authors:  R J Lilford; D Braunholtz
Journal:  J Epidemiol Community Health       Date:  2000-10       Impact factor: 3.710

4.  Comparison of Bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients.

Authors:  John S Troiani; Bradley P Carlin
Journal:  Stat Med       Date:  2004-03-15       Impact factor: 2.373

5.  Policy relevance of Bayesian statistics overestimated?

Authors:  Gert Jan van der Wilt; Maroeska Rovers; Huub Straatman; Sjoukje van der Bij; Paul van den Broek; Gerhard Zielhuis
Journal:  Int J Technol Assess Health Care       Date:  2004       Impact factor: 2.188

6.  Bayesian perspectives for epidemiological research: I. Foundations and basic methods.

Authors:  Sander Greenland
Journal:  Int J Epidemiol       Date:  2006-01-30       Impact factor: 7.196

7.  A rational framework for decision making by the National Institute For Clinical Excellence (NICE).

Authors:  Karl Claxton; Mark Sculpher; Michael Drummond
Journal:  Lancet       Date:  2002-08-31       Impact factor: 79.321

8.  Prime time for Bayes.

Authors:  J B Kadane
Journal:  Control Clin Trials       Date:  1995-10

9.  Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-Bayesian methods.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharm Sci       Date:  1982-12       Impact factor: 3.534

10.  A comparison of Bayesian and maximum likelihood methods to determine the performance of a point of care test for Helicobacter pylori in the office setting.

Authors:  Brendan C Delaney; Roger L Holder; Teresa F Allan; Joyce E Kenkre; F D Richard Hobbs
Journal:  Med Decis Making       Date:  2003 Jan-Feb       Impact factor: 2.583

View more
  2 in total

1.  Differences between Frequentist and Bayesian inference in routine surveillance for influenza vaccine effectiveness: a test-negative case-control study.

Authors:  Michael L Jackson; Jill Ferdinands; Mary Patricia Nowalk; Richard K Zimmerman; Burney Kieke; Manjusha Gaglani; Kempapura Murthy; Joshua G Petrie; Emily T Martin; Jessie R Chung; Brendan Flannery; Lisa A Jackson
Journal:  BMC Public Health       Date:  2021-03-16       Impact factor: 3.295

2.  When data interpretation should not rely on the magnitude of P values: the example of ANDROMEDA SHOCK trial.

Authors:  Francesco Corradi; Guido Tavazzi; Gregorio Santori; Francesco Forfori
Journal:  Ann Transl Med       Date:  2020-06
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.