Cost-utility analysis when not everyone wants the treatment: modelling split-choice bias

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Cost-utility analysis when not everyone wants the treatment: modelling split-choice bias. / Lilford, Richard; Girling, Alan; Braunholtz, D; Gillett, W; Gordon, J; Taylor, Celia; Stevens, Andrew.

In: Medical Decision Making, Vol. 27, 01.01.2007, p. 21-26.

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@article{e159972ac41b4f44b660737fa4b8de6a,
title = "Cost-utility analysis when not everyone wants the treatment: modelling split-choice bias",
abstract = "Not all clinically eligible patients will necessarily accept a new treatment. Cost-utility analysis recognizes this by multiplying the mean incremental expected utility (EU) by the participation rate to obtain the utility gain per head. However, the mean EU gain over all patients in a defined clinical category is traditionally used as a proxy for the mean EU gain over the subpopulation of acceptors. Even for clinically identical patients, this may lead to a biased assessment of total benefit because a patient motivated to accept the new treatment is likely to value its effects more favorably than a patient who declines. An analysis that ignores this tendency will be biased toward an underestimate of true benefits of a health technology (HT). The extent of this bios is described within a quality-adjusted life year-based utility model for a population of clinically indistinguishable patients who differ with respect to the values that they place on the possible health outcomes of an HT The size of the bias is sensitive to the proportion of patients who accept the treatment, under both deterministic and probabilistic models of individual decision making. In all cases in which decision making is correlated with personal utility gain, the bias rises steeply as the proportion of acceptors declines.",
keywords = "rationing, patient choice, cost-utility analysis, QALY, health technology assessment, decision analysis, split-choice bias",
author = "Richard Lilford and Alan Girling and D Braunholtz and W Gillett and J Gordon and Celia Taylor and Andrew Stevens",
year = "2007",
month = jan,
day = "1",
doi = "10.1177/0272989X06297099",
language = "English",
volume = "27",
pages = "21--26",
journal = "Medical Decision Making",
issn = "0272-989X",
publisher = "SAGE Publications",

}

RIS

TY - JOUR

T1 - Cost-utility analysis when not everyone wants the treatment: modelling split-choice bias

AU - Lilford, Richard

AU - Girling, Alan

AU - Braunholtz, D

AU - Gillett, W

AU - Gordon, J

AU - Taylor, Celia

AU - Stevens, Andrew

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Not all clinically eligible patients will necessarily accept a new treatment. Cost-utility analysis recognizes this by multiplying the mean incremental expected utility (EU) by the participation rate to obtain the utility gain per head. However, the mean EU gain over all patients in a defined clinical category is traditionally used as a proxy for the mean EU gain over the subpopulation of acceptors. Even for clinically identical patients, this may lead to a biased assessment of total benefit because a patient motivated to accept the new treatment is likely to value its effects more favorably than a patient who declines. An analysis that ignores this tendency will be biased toward an underestimate of true benefits of a health technology (HT). The extent of this bios is described within a quality-adjusted life year-based utility model for a population of clinically indistinguishable patients who differ with respect to the values that they place on the possible health outcomes of an HT The size of the bias is sensitive to the proportion of patients who accept the treatment, under both deterministic and probabilistic models of individual decision making. In all cases in which decision making is correlated with personal utility gain, the bias rises steeply as the proportion of acceptors declines.

AB - Not all clinically eligible patients will necessarily accept a new treatment. Cost-utility analysis recognizes this by multiplying the mean incremental expected utility (EU) by the participation rate to obtain the utility gain per head. However, the mean EU gain over all patients in a defined clinical category is traditionally used as a proxy for the mean EU gain over the subpopulation of acceptors. Even for clinically identical patients, this may lead to a biased assessment of total benefit because a patient motivated to accept the new treatment is likely to value its effects more favorably than a patient who declines. An analysis that ignores this tendency will be biased toward an underestimate of true benefits of a health technology (HT). The extent of this bios is described within a quality-adjusted life year-based utility model for a population of clinically indistinguishable patients who differ with respect to the values that they place on the possible health outcomes of an HT The size of the bias is sensitive to the proportion of patients who accept the treatment, under both deterministic and probabilistic models of individual decision making. In all cases in which decision making is correlated with personal utility gain, the bias rises steeply as the proportion of acceptors declines.

KW - rationing

KW - patient choice

KW - cost-utility analysis

KW - QALY

KW - health technology assessment

KW - decision analysis

KW - split-choice bias

U2 - 10.1177/0272989X06297099

DO - 10.1177/0272989X06297099

M3 - Article

C2 - 17237449

VL - 27

SP - 21

EP - 26

JO - Medical Decision Making

JF - Medical Decision Making

SN - 0272-989X

ER -