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Patient Care under Uncertainty /
"Although uncertainty is common in patient care, it has not been largely addressed in research on evidence-based medicine. Patient Care Under Uncertainty strives to correct this huge omission. For the past few years, renowned economist Charles Manski has been applying the statistical tools of e...
| Autor principal: | |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, New Jersey :
Princeton University Press,
[2019]
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| Colección: | Book collections on Project MUSE.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Surveillance or Aggressive Treatment; Evolution of the Book; Summary; 1: Clinical Guidelines and Clinical Judgment; 1.1. Adherence to Guidelines or Exercise of Judgment?; Variation in Guidelines; Case Study: Nodal Observation or Dissection in Treatment of Melanoma; 1.2. Degrees of Personalized Medicine; Prediction of Cardiovascular Disease; The Breast Cancer Risk Assessment Tool; Predicting Unrealistically Precise Probabilities; 1.3. Optimal Care Assuming Rational Expectations; Optimal Choice between Surveillance and Aggressive Treatment 1.4. Psychological Research Comparing Evidence-Based Prediction and Clinical Judgment1.5. Second-Best Welfare Comparison of Adherence to Guidelines and Clinical Judgment; Surveillance or Aggressive Treatment of Women at Risk of Breast Cancer; 2: Wishful Extrapolation from Research to Patient Care; 2.1. From Study Populations to Patient Populations; Trials of Drug Treatments for Hypertension; Campbell and the Primacy of Internal Validity; 2.2. From Experimental Treatments to Clinical Treatments; Intensity of Treatment; Blinding in Drug Trials; 2.3. From Measured Outcomes to Patient Welfare
- Interpreting Surrogate OutcomesAssessing Multiple Outcomes; 2.4. From Hypothesis Tests to Treatment Decisions; Using Hypothesis Tests to Compare Treatments; Using Hypothesis Tests to Choose When to Report Findings; 2.5. Wishful Meta-Analysis of Disparate Studies; A Meta-Analysis of Outcomes of Bariatric Surgery; The Misleading Rhetoric of Meta-Analysis; The Algebraic Wisdom of Crowds; 2.6. Sacrificing Relevance for Certitude; 3: Credible Use of Evidence to Inform Patient Care; 3.1. Identification of Treatment Response; Unobservability of Counterfactual Treatment Outcomes; Trial Data Observational DataTrials with Imperfect Compliance; Extrapolation Problems; Missing Data and Measurement Errors; 3.2. Studying Identification; 3.3. Identification with Missing Data on Patient Outcomes or Attributes; Missing Data in a Trial of Treatments for Hypertension; Missing Data on Family Size When Predicting Genetic Mutations; 3.4. Partial Personalized Risk Assessment; Predicting Mean Remaining Life Span; 3.5. Credible Inference with Observational Data; Bounds with No Knowledge of Counterfactual Outcomes; Sentencing and Recidivism; Assumptions Using Instrumental Variables Case Study: Bounding the Mortality Effects of Swan-Ganz Catheterization3.6. Identification of Response to Testing and Treatment; Optimal Testing and Treatment; Identification of Testing and Treatment Response with Observational Data; Measuring the Accuracy of Diagnostic Tests; 3.7. Prediction Combining Multiple Studies; Combining Multiple Breast Cancer Risk Assessments; Combining Partial Predictions; 4: Reasonable Care under Uncertainty; 4.1. Qualitative Recognition of Uncertainty; 4.2. Formalizing Uncertainty; States of Nature; 4.3. Optimal and Reasonable Decisions