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Cost-effectiveness with multiple outcomes.

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TLDR
An approach to cost-effectiveness analysis where artificial aggregation is avoided is proposed, assigning to each activity the weights which are the most favourable in a comparison with the other options available, so that activities which have a poor score in this method are guaranteed to be inferior.
Abstract
In a large number of situations, activities in health care have to be measured in terms of outcome and cost. However, the cases where outcome is fully captured by a single measure are rather few, so that one uses some index for outcome, computed by weighing together several outcome measures using subjective and somewhat arbitrary weights. In the paper we propose an approach to cost-effectiveness analysis where such artificial aggregation is avoided. This is achieved by assigning to each activity the weights which are the most favourable in a comparison with the other options available, so that activities which have a poor score in this method are guaranteed to be inferior. The method corresponds to applying Data envelopment analysis, known from the theory of productivity, to the context of health economic evaluations. The method is applied to an analysis of the cost-effectiveness of alternative health plans using data from the Medical Outcome Study (JAMA 1996; 276: 1039-1047), where outcome is measured as improvement in mental and physical health.

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Citations
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The efficiency of government spending on health: Evidence from Europe and Central Asia

TL;DR: In this paper, the authors used data envelopment analysis (DEA) to evaluate health expenditures to demonstrate how productivity has changed over time for 46 selected countries in Europe and Central Asia.
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Better informing decision making with multiple outcomes cost-effectiveness analysis under uncertainty in cost-disutility space.

TL;DR: Comparison in CDU space and associated summary measures have distinct advantages to multiple domain comparisons, aiding transparent and robust joint comparison of costs and multiple effects under uncertainty across potential threshold values for effect, better informing net benefit assessment and related reimbursement and research decisions.
References
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Journal ArticleDOI

Measuring the efficiency of decision making units

TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
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A procedure for ranking efficient units in data envelopment analysis

TL;DR: In this paper, a modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed, which provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.
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The Medical Outcomes Study. An application of methods for monitoring the results of medical care

TL;DR: The Medical Outcomes Study was designed to determine whether variations in patient outcomes are explained by differences in system of care, clinician specialty, and clinicians' technical and interpersonal styles and develop more practical tools for the routine monitoring of patient outcomes in medical practice.
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Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation.

TL;DR: It is concluded that percentile bootstrap confidence interval methods provide a promising approach to estimating the uncertainty of ICER point estimates, however, successive bootstrap estimates of bias and standard error suggests that these may be unstable; accordingly, it is strongly recommend a cautious interpretation of such estimates.
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Differences in 4-year health outcomes for elderly and poor, chronically ill patients treated in HMO and fee-for-service systems. Results from the Medical Outcomes Study.

TL;DR: During the study period, elderly and poor chronically ill patients had worse physical health outcomes in HMOs than in FFS systems; mental health outcomes varied by study site and patient characteristics.
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