A framework for addressing structural uncertainty in decision models

Christopher H Jackson; Laura Bojke; Simon G Thompson; Karl Claxton; Linda D Sharples

doi:10.1177/0272989X11406986

A framework for addressing structural uncertainty in decision models

Christopher H Jackson, Laura Bojke, Simon G Thompson, Karl Claxton, Linda D Sharples

Research output: Contribution to journal › Article › peer-review

Abstract

Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of cost-effectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of cost-effectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis.

Original language	English
Pages (from-to)	662-674
Number of pages	13
Journal	Medical Decision Making
Volume	31
Issue number	4
DOIs	https://doi.org/10.1177/0272989X11406986
Publication status	Published - Jul 2011

Access to Document

10.1177/0272989X11406986

http://mdm.sagepub.com/content/31/4/662

Cite this

@article{1b85b7e6057941c7864692e423530c6e,

title = "A framework for addressing structural uncertainty in decision models",

abstract = "Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of cost-effectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of cost-effectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis.",

author = "Jackson, {Christopher H} and Laura Bojke and Thompson, {Simon G} and Karl Claxton and Sharples, {Linda D}",

year = "2011",

month = jul,

doi = "10.1177/0272989X11406986",

language = "English",

volume = "31",

pages = "662--674",

journal = "Medical Decision Making",

issn = "0272-989X",

publisher = "SAGE",

number = "4",

}

TY - JOUR

T1 - A framework for addressing structural uncertainty in decision models

AU - Jackson, Christopher H

AU - Bojke, Laura

AU - Thompson, Simon G

AU - Claxton, Karl

AU - Sharples, Linda D

PY - 2011/7

Y1 - 2011/7

N2 - Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of cost-effectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of cost-effectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis.

AB - Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of cost-effectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of cost-effectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis.

UR - http://www.scopus.com/inward/record.url?scp=79960522971&partnerID=8YFLogxK

U2 - 10.1177/0272989X11406986

DO - 10.1177/0272989X11406986

M3 - Article

C2 - 21602487

SN - 0272-989X

VL - 31

SP - 662

EP - 674

JO - Medical Decision Making

JF - Medical Decision Making

IS - 4

ER -

A framework for addressing structural uncertainty in decision models

Abstract

Access to Document

Other files and links

Cite this