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Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies

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Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. / Pertile, Paolo; Forster, Martin; La Torre, Davide.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 177, No. 2, 02.2014, p. 419-438.

Research output: Contribution to journalArticle

Harvard

Pertile, P, Forster, M & La Torre, D 2014, 'Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies', Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 177, no. 2, pp. 419-438. https://doi.org/10.1111/rssa.12025

APA

Pertile, P., Forster, M., & La Torre, D. (2014). Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177(2), 419-438. https://doi.org/10.1111/rssa.12025

Vancouver

Pertile P, Forster M, La Torre D. Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2014 Feb;177(2):419-438. https://doi.org/10.1111/rssa.12025

Author

Pertile, Paolo ; Forster, Martin ; La Torre, Davide. / Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 2014 ; Vol. 177, No. 2. pp. 419-438.

Bibtex - Download

@article{46173f901aca493aae25596ce218fd46,
title = "Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies",
abstract = "We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.",
keywords = "Bayesian, Dynamic programming, Economic evaluation, Sequential sampling",
author = "Paolo Pertile and Martin Forster and {La Torre}, Davide",
year = "2014",
month = "2",
doi = "10.1111/rssa.12025",
language = "English",
volume = "177",
pages = "419--438",
journal = "Journal of the Royal Statistical Society: Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "Wiley",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies

AU - Pertile, Paolo

AU - Forster, Martin

AU - La Torre, Davide

PY - 2014/2

Y1 - 2014/2

N2 - We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.

AB - We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.

KW - Bayesian

KW - Dynamic programming

KW - Economic evaluation

KW - Sequential sampling

U2 - 10.1111/rssa.12025

DO - 10.1111/rssa.12025

M3 - Article

VL - 177

SP - 419

EP - 438

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

SN - 0964-1998

IS - 2

ER -