Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies

O'Hagan, A; Stevens, J W
December 2002
Statistical Methods in Medical Research;Dec2002, Vol. 11 Issue 6, p469
Academic Journal
journal article
We review the development of Bayesian statistical methods for the design and analysis of randomized controlled trials in the assessment of the cost-effectiveness of health care technologies. We place particular emphasis on the benefits of the Bayesian approach; the implications of skew cost data; the need to model the data appropriately to generate efficient and robust inferences instead of relying on distribution-free methods; the importance of making full use of quantitative and structural prior information to produce realistic inferences; and issues in the determination of sample size. Several new examples are presented to illustrate the methods. We conclude with a discussion of the key areas for future research.


Related Articles

  • Frequentist and Bayesian approaches for interval-censored data. Gómez, Guadalupe; Calle, M. Luz; Oller, Ramon // Statistical Papers;Apr2004, Vol. 45 Issue 2, p139 

    Interval censoring appears when the event of interest is only known to have occurred within a random time interval. Estimation and hypothesis testing procedures for interval-censored data are surveyed. We distinguish between frequentist and Bayesian approaches. Computational aspects for every...

  • Stata command for calculating adverse event and efficacy stopping boundaries for phase II single-arm trials. Fellman, Bryan M. // Stata Journal;2014, Vol. 14 Issue 2, p407 

    Many programs and functions in statistical packages focus on the final stage of clinical trials, that is, the data analysis. In this article, I aim to assist in the early stages of clinical trials, specifically, the design of phase II single-arm trials. I present the new command stopbound, which...

  • A Bayesian approach to classification accuracy inference. Magnussen, Steen // Forestry: An International Journal of Forest Research;2009, Vol. 82 Issue 2, p211 

    Bayesian accuracy assessments draw inference about random (super-population) parameters characterizing the classification process and accuracy statistics derived from these parameters. A conventional frequentist approach seeks to estimate the same parameters, but view them as fixed finite...

  • Bayesian Prediction of the Overhaul Effect on a Repairable System with Bounded Failure Intensity. Srivastava, Preeti Wanti; Jain, Nidhi // International Journal of Quality, Statistics & Reliability;2011, p1 

    This paper deals with the Bayes prediction of the future failures of a deteriorating repairable mechanical system subject to minimal repairs and periodic overhauls. To model the effect of overhauls on the reliability of the systema proportional age reduction model is assumed and the 2-parameter...

  • Importance timing. Skilling, John // AIP Conference Proceedings;Aug2013, Vol. 1553 Issue 1, p171 

    Bayesian evidence Z = ∫ L(x)dπ(x) is defined as likelihood L integrated over prior π, and is often computed in that form - with nested sampling as the preferred algorithm for passing from prior to posterior in large or complicated applications. However, a user may suspect that some...

  • Hierarchical Bayes methods for systems with spatially varying condition states. Dann, Markus; Maes, Marc A. // Canadian Journal of Civil Engineering;Oct2007, Vol. 34 Issue 10, p1289 

    In engineering decision making, we often face problems where the conditions governing certain response models vary spatially. In such cases, the use of hierarchical Bayesian models is often beneficial. Such models are based on a “condition state” vector that is assumed to be...

  • A prior for steepness in stock-recruitment relationships, based on an evolutionary persistence principle. Xi He; Mangel, Marc; MacCall, Alec // Fishery Bulletin;Jul2006, Vol. 104 Issue 3, p428 

    Priors are existing information or beliefs that are needed in Bayesian analysis. Informative priors are important in obtaining the Bayesian posterior distributions for estimated parameters in stock assessment. In the case of the steepness parameter (h), the need for an informative prior is...

  • Bayesian probability. Maher, Patrick // Synthese;Jan2010, Vol. 172 Issue 1, p119 

    Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation...

  • Some Faults of the Bayes Factor in Nonparametric Model Selection. Carota, Cinzia // Statistical Methods & Applications;2006, Vol. 15 Issue 1, p37 

    We compare different Bayesian strategies for testing a parametric model versus a nonparametric alternative on the ground of their ability to solve the inconsistency problems arising when using the Bayes factor under certain conditions. A preliminary critical discussion of such an inconsistency...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics