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R F Nease

Publications and source records attributed to R F Nease.

51 records · Page 3Linked to original sources
PubMedDate not supplied

Choice-matching preference reversals in health outcome assessments.

BACKGROUND: Health outcome utility assessments generally assume procedural invariance. Preference reversals violating procedural invariance occur in economic scenarios when the assessment process shifts from a choice to a fill-in-the-blank task. PURPOSE: To determine if similar reversals occur in utility assessments. METHODS: One hundred thirty-six volunteer subjects completed 6 preference assessments of 4 personal health scenarios. Patients responded to otherwise identical tasks using either choice or fill-in-the-blank processes in a randomized crossover design. The authors determined the percentage of subjects preferring, or inferred to prefer, a given choice. RESULTS: Preference reversals occurred in all assessment scenarios. CONCLUSIONS: These preference reversals are a potential source of confusion for utility assessment and informed consent. They could be manipulated to achieve ends other than the best interest of patients. Anchoring or the prominence hypothesis may explain these findings.

Adult↗
PubMedDate not supplied

Solid recommendations from soft numbers: the test/treatment decision.

The authors review the probability threshold approach to test/treatment decisions developed by Pauker and Kassirer, emphasizing that certain aspects of the nature of medical decisions call for a new approach. The utility threshold approach, while maintaining all the advantages of threshold methods in general, brings improvements. It diminishes the need to accurately assess one of the decision's parameters: the patient's utility for the outcome states. For a simple case of one disease with three outcome states (cured, diseased, dead) and one test, three utility thresholds are derived. The treat/no treat threshold, denoted by u, separates the utility space in two. If the patient's value for the diseased state is greater than u, the analyst can feel confident in recommending the patient forego treatment. Similar interpretations are developed for u1, the no treatment/test utility threshold (the value u must take, given a positive test result, for the patient to be indifferent between foregoing and receiving treatment), and u2, the test/treatment utility threshold (the value u must take, given a negative test result, for the patient to be indifferent between foregoing and receiving treatment.

Decision Trees↗
PubMedDate not supplied

Threshold analysis using diagnostic tests with multiple results.

Clinical problems represented by decision trees can be analyzed in terms of the probability threshold model, which provides management recommendations based on the prior probability of disease, the test threshold, and the test-treatment threshold. As originally proposed, the threshold model assumes that diagnostic tests provide information about a single event that is relevant to the decision. For some problems, however, a diagnostic test may provide information about more than one such event (e.g., a computed tomography [CT] scan gives information about both mediastinal and hilar metastases in lung cancer). The authors extend the probability threshold model to cases in which a single test provides information about two events that are relevant to the decision. They derive four thresholds that determine the best strategy for any combination of test results. The approach is illustrated for the decision to use a CT scan to stage lung cancer. The analysis reveals that: 1) the range of prior probabilities for which testing is optimal increases; 2) for some prior probabilities only test results about one event are important; 3) for some prior probabilities test results about both events are important; and 4) failure to account fully for information provided by a test can lead to erroneous test and treatment recommendations.

Bayes Theorem↗
PubMedDate not supplied

A method for estimating the cost-effectiveness of incorporating patient preferences into practice guidelines.

Many clinical practice guidelines fail to account for the preferences of the individual patient. Approaches that seek to include the preferences of the individual patient in the decision-making process (e.g., interactive videodisks for patient education), however, may incur substantial incremental costs. Developers of clinical practice guidelines must therefore determine whether it is appropriate to make their guidelines flexible with regard to patient preferences. The authors present a formal method for determining the cost-effectiveness of incorporating the preferences of individual patients into clinical practice guidelines. Based on utilities assessed from 37 patients, they apply the method in the setting of mild hypertension. In this example, they estimate that the cost-effectiveness ratio for individualized utility assessment is $48,565 per quality-adjusted year of life, a ratio that compares favorably with other health interventions that are promoted actively. This approach, which can be applied to any clinical domain, offers a formal method for determining whether the incorporation of individual patient preferences is important clinically and is justified economically.

Adult↗
PubMedDate not supplied

Do violations of the axioms of expected utility theory threaten decision analysis?

Research demonstrates that people violate the independence principle of expected utility theory, raising the question of whether expected utility theory is normative for medical decision making. The author provides three arguments that violations of the independence principle are less problematic than they might first appear. First, the independence principle follows from other more fundamental axioms whose appeal may be more readily apparent than that of the independence principle. Second, the axioms need not be descriptive to be normative, and they need not be attractive to all decision makers for expected utility theory to be useful for some. Finally, by providing a metaphor of decision analysis as a conversation between the actual decision maker and a model decision maker, the author argues that expected utility theory need not be purely normative for decision analysis to be useful. In short, violations of the independence principle do not necessarily represent direct violations of the axioms of expected utility theory; behavioral violations of the axioms of expected utility theory do not necessarily imply that decision analysis is not normative; and full normativeness is not necessary for decision analysis to generate valuable insights.

Bias↗
PubMedDate not supplied

Determining transition probabilities from mortality rates and autopsy findings.

The Markov process is a useful tool for modeling the natural history of disease, which is becoming increasingly important as new diagnostic tests increase the detectability of early-stage disease. The accuracy of a Markov model, however, depends on the accuracy of the estimates for the transition probabilities between different stages of disease. Because these estimates are usually based on "expert opinion" or small cohort studies, they are subject to imprecision and bias. The authors describe an alternative method of estimating transition probabilities from the stage distribution of disease observed at the time of death and age-specific mortality rates from other causes. In addition, they prove that the transition probabilities are unique given certain assumptions about how they change with age. Finally, they illustrate the method using population-based data for prostate cancer.

Adult↗
PubMedDate not supplied

Representation and analysis of medical decision problems with influence diagrams.

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis.

AIDS-Related Opportunistic Infections↗
PubMedDate not supplied

Use of influence diagrams to structure medical decisions.

Influence diagrams are compact representations of decision problems that are mathematically equivalent to decision trees. The authors present five important principles for structuring a decision as an influence diagram: 1) start at the value node and work back to the decision nodes; 2) draw the arcs in the direction that makes the probabilities easiest to assess; 3) use informational arcs to specify which events will have been observed at the time each decision is made; 4) ensure that missing arcs reflect intentional assertions about conditional independence and the timing of observations; and 5) ensure that there are no cycles in the influence diagram. They then build an influence diagram for the problem of staging non-small-cell lung cancer as an illustration. Influence diagrams offer several strengths for structuring medical decisions. They represent graphically and compactly the probabilistic relationships between parameters in the model. Influence diagrams also allow the model to be structured in a fashion that eases the necessary probability assessments, regardless of whether the assessments are based on available evidence or on expert judgment. Influence diagrams provide an important complement to decision trees, especially for representing probabilistic relationships among variables in a decision model.

Carcinoma, Non-Small-Cell Lung↗
PubMedDate not supplied

A normative analytic framework for development of practice guidelines for specific clinical populations.

BACKGROUND: A central problem in practice guideline development is how to develop guidelines that appropriately account for variations in clinical populations and practice settings. Despite recognition of this problem, there is no formal mechanism for assessing what the need is for flexibility in guidelines, or for deciding how to incorporate such flexibility into recommendations. OBJECTIVE: This research sought to provide a formal basis to determine when clinical circumstances vary sufficiently that guideline recommendations should differ, how recommendations should be tailored for a specific clinical setting, and whether the benefit associated with such site-specific guidelines justifies the expense of their development. RESULTS: The authors describe an approach for estimating the maximum health benefit that developers can obtain by eliminating uncertainty about differences in the patient populations and practice settings in which a guideline will be used. This estimate, the expected value of customization, provides a mechanism to evaluate the cost-effectiveness of the development of site-specific guidelines that account explicitly for variation in clinical circumstances. Application of this method to the development of screening guidelines for human immunodeficiency virus (HIV) infection indicates that the development of site-specific guidelines potentially is cost-effective. Site-specific guidelines either improve, or leave unchanged, the efficiency of HIV screening; whether they increase or decrease total expenditures and health benefits depends on the choice of a cost-effectiveness threshold, and the clinical problem. CONCLUSIONS: Development of guideline recommendations based on decision models provides a normative approach for evaluating the need for and the cost-effectiveness of site-specific guidelines that have been tailored to specific practice settings. Such site-specific guidelines can improve substantially the expected health benefit and the economic efficiency of practice guidelines.

Cost-Benefit Analysis↗