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Author: Paul Meehl Publisher: Echo Point Books & Media ISBN: 9781626542303 Category : Medical Languages : en Pages : 164
Book Description
"Clinical versus Statistical Prediction" is Paul Meehl's famous examination of benefits and disutilities related to the different ways of combining information to make predictions. It is a clarifying analysis as relevant today as when it first appeared. A major methodological problem for clinical psychology concerns the relation between clinical and actuarial methods of arriving at diagnoses and predicting behavior. Without prejudging the question as to whether these methods are fundamentally different, we can at least set forth the obvious distinctions between them in practical applications. The problem is to predict how a person is going to behave: What is the most accurate way to go about this task? "Clinical versus Statistical Prediction" offers a penetrating and thorough look at the pros and cons of human judgment versus actuarial integration of information as applied to the prediction problem. Widely considered the leading text on the subject, Paul Meehl's landmark analysis is reprinted here in its entirety, including his updated preface written forty-two years after the first publication of the book. This classic work is a must-have for students and practitioners interested in better understanding human behavior, for anyone wanting to make the most accurate decisions from all sorts of data, and for those interested in the ethics and intricacies of prediction. As Meehl puts it, " "When one is dealing with human lives and life opportunities, it is immoral to adopt a mode of decision-making which has been demonstrated repeatedly to be either inferior in success rate or, when equal, costlier to the client or the taxpayer.""
Author: Paul Meehl Publisher: Echo Point Books & Media ISBN: 9781626542303 Category : Medical Languages : en Pages : 164
Book Description
"Clinical versus Statistical Prediction" is Paul Meehl's famous examination of benefits and disutilities related to the different ways of combining information to make predictions. It is a clarifying analysis as relevant today as when it first appeared. A major methodological problem for clinical psychology concerns the relation between clinical and actuarial methods of arriving at diagnoses and predicting behavior. Without prejudging the question as to whether these methods are fundamentally different, we can at least set forth the obvious distinctions between them in practical applications. The problem is to predict how a person is going to behave: What is the most accurate way to go about this task? "Clinical versus Statistical Prediction" offers a penetrating and thorough look at the pros and cons of human judgment versus actuarial integration of information as applied to the prediction problem. Widely considered the leading text on the subject, Paul Meehl's landmark analysis is reprinted here in its entirety, including his updated preface written forty-two years after the first publication of the book. This classic work is a must-have for students and practitioners interested in better understanding human behavior, for anyone wanting to make the most accurate decisions from all sorts of data, and for those interested in the ethics and intricacies of prediction. As Meehl puts it, " "When one is dealing with human lives and life opportunities, it is immoral to adopt a mode of decision-making which has been demonstrated repeatedly to be either inferior in success rate or, when equal, costlier to the client or the taxpayer.""
Author: Pieter Kubben Publisher: Springer ISBN: 3319997130 Category : Medical Languages : en Pages : 219
Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author: Institute of Medicine Publisher: National Academies Press ISBN: 0309171148 Category : Medical Languages : en Pages : 221
Book Description
Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
Author: Howard N. Garb Publisher: Amer Psychological Assn ISBN: 9781557984838 Category : Medical Languages : en Pages : 333
Book Description
...a comprehensive, empirical investigation of when biases are likely to occur...recommends the use of non-intuitive decision aids to assure the validity of clinical judgements. ..a must read for all helping professionals.
Author: Thomas A. Gerds Publisher: CRC Press ISBN: 0429764235 Category : Mathematics Languages : en Pages : 249
Book Description
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.
Author: Jerry Z. Muller Publisher: Princeton University Press ISBN: 0691191263 Category : Business & Economics Languages : en Pages : 245
Book Description
How the obsession with quantifying human performance threatens business, medicine, education, government—and the quality of our lives Today, organizations of all kinds are ruled by the belief that the path to success is quantifying human performance, publicizing the results, and dividing up the rewards based on the numbers. But in our zeal to instill the evaluation process with scientific rigor, we've gone from measuring performance to fixating on measuring itself—and this tyranny of metrics now threatens the quality of our organizations and lives. In this brief, accessible, and powerful book, Jerry Muller uncovers the damage metrics are causing and shows how we can begin to fix the problem. Filled with examples from business, medicine, education, government, and other fields, the book explains why paying for measured performance doesn't work, why surgical scorecards may increase deaths, and much more. But Muller also shows that, when used as a complement to judgment based on personal experience, metrics can be beneficial, and he includes an invaluable checklist of when and how to use them. The result is an essential corrective to a harmful trend that increasingly affects us all.
Author: R. Karl Hanson Publisher: American Psychological Association (APA) ISBN: 9781433836411 Category : Psychology Languages : en Pages : 440
Book Description
"As statistical prediction becomes ubiquitous in many areas of psychology, a comprehensive guide to navigating these tools is needed, one that covers topics pertinent to those in psychology and the social sciences. Prediction Statistics for Psychological Assessment, by R. Karl Hanson, is the first book to teach students and practitioners the nuts and bolts of prediction statistics, while illustrating the utility of prediction and prediction tools in applied psychological practice. This valuable resource uses real-world examples, helpful explanations and practice exercises to support the use of prediction tools in psychological assessment. Actuarial risk assessment evaluators need to know how prediction tools work, how to evaluate them, and how to interpret their results in applied assessments. Written in a clear and accessible manner, this user-friendly book helps readers understand how to evaluate and interpret different kinds of prediction tools, appreciate the numeric information used in risk communication, and utilize prediction tools to inform evidence-based decision-making"--
Author: Agency for Health Care Research and Quality (U.S.) Publisher: Government Printing Office ISBN: 1587634236 Category : Medical Languages : en Pages : 236
Book Description
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Author: Paul Meehl
Author: Paul Meehl
Author: Pieter Kubben
Author: Institute of Medicine
Author: Howard N. Garb
Author: Thomas A. Gerds
Author: Joseph Tal
Author: Paul Everett Meehl
Author: Jerry Z. Muller
Author: R. Karl Hanson
Author: Agency for Health Care Research and Quality (U.S.)