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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Manski, Charles F. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton, New Jersey : Princeton University Press, [2019]
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Manski, Charles F.,  |e author. 
245 1 0 |a Patient care under uncertainty /  |c Charles F. Manski. 
264 1 |a Princeton, New Jersey :  |b Princeton University Press,  |c [2019] 
264 4 |c ©2019 
300 |a 1 online resource (xiii, 166 pages) 
336 |a text  |b txt  |2 rdacontent 
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504 |a Includes bibliographical references (pages 149-157) and index. 
520 |a "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 economics to decision making under uncertainty in the context of patient health status and response to treatment. In the precise language of econometrics, "uncertainty" means that the available evidence and knowledge do not suffice to yield precise probabilistic predictions. In the health-care sphere, the most common example is a choice between periodic surveillance or aggressive treatment of patients at risk of a potential disease. Manski examines the subject by applying the economic principals of personalized risk assessment to research on treatment response. Through his work as an econometrician, Manski shows how statistical imprecision and identification problems affect empirical research in the patient care sphere. In the book, Manski reviews continuing discourse in medicine and critiques how evidence from randomized clinical trials has been used to inform decision making. He describes research on identification, develops decision-theoretic principles for reasonable care under uncertainty, and offers suggestions for sensible decision-making with sample data from randomized trials. Manski ends by reviewing patient care from a public-health perspective and considering management of uncertainty in drug approval. In terms of patient care, Manski seeks to help clinicians, public health planners, and patients recognize and cope with uncertainty when making decisions about patient care"--Provided by publisher 
588 0 |a Online resource; title from digital title page (viewed on June 29, 2019). 
505 0 |a 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 
505 8 |a 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 
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