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D M Eddy

Publications and source records attributed to D M Eddy.

96 records · Page 6Linked to original sources
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An introduction to a Bayesian method for meta-analysis: The confidence profile method.

The Confidence Profile Method is a new Bayesian method that can be used to assess technologies where the available evidence involves a variety of experimental designs, types of outcomes, and effect measures; a variety of biases; combinations of biases and nested bases; uncertainty about biases; an underlying variability in the parameter of interest; indirect evidence; and technology families. The result of an analysis with the Confidence Profile Method is a posterior distribution for the parameter of interest, posterior distributions for other parameters, and a covariance matrix for all the parameters in the model. The posterior distributions incorporate all the uncertainty the assessor chooses to describe about any of the parameters used in the analysis.

Bayes Theorem↗
PubMedDate not supplied

The confidence profile method: a Bayesian method for assessing health technologies.

The Confidence Profile Method is a Bayesian method for adjusting and combining pieces of evidence to estimate parameters, such as the effect of health technologies on health outcomes. The information in each piece of evidence is captured in a likelihood function that gives the likelihood of the observed results of the evidence as a function of possible values of the parameter. A posterior distribution is calculated from Bayes formula as the product of the likelihood function and a prior distribution. Multiple pieces of evidence are incorporated by successive applications of Bayes' formula. Pieces of evidence are adjusted for biases to internal or external validity by modeling the biases and deriving "adjusted" likelihood functions that incorporate the models. Likelihood functions have been derived for one-, two- and multi-arm prospective studies; 2 x 2, 2 x n and matched case-control studies, and cross-sectional studies. Biases that can be incorporated in likelihood functions include crossover in controlled trials, error in measurement outcomes, patient selection biases, differences in technologies, and differences in length of follow-up. Effect measures include differences of rates, ratios of rates, and odds ratios. The elements of the method are illustrated with an analysis of the effect of a thrombolytic agent on the difference in probability of 1-year survival after a heart attack.

Bayes Theorem↗
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Performance measurement: problems and solutions.

Recent efforts to measure performance have established its feasibility and value. However, its full potential is currently limited by several problems. They include the probabilistic nature, rarity, and confounding of many health outcomes; the inadequacies of information systems; the multiplicity of measures and measures; the complexity of health plans; and the availability of funding. Solutions are to rely more on process measures; to justify every measure with a formal, evidence-based rationale; to improve information systems; to supplement population-based measures with case-based measures; to develop a single, nationally standardized set of measures; and to provide nonpolitical, public funding for the design and administration of measures.

Health Benefit Plans, Employee↗
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What does it cost, and does it help the patient?. Interview by Wesley Curry.

At the ACPE National Conference in San Antonio on May 17, 1990, David M. Eddy, MD, PhD, described serious problems with the information infrastructure for medicine, and discussed the implications of these failings for the quality and cost of medical care. Physician Executive discussed the issues raised in Dr. Eddy's presentation and reports his comments in this article. Dr. Eddy is Professor of Health Policy and Management, Duke University, Durham, N.C.

Decision Making↗