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Author: Paul Meehl
Publisher: Echo Point Books & Media
ISBN: 9781626542303
Category : Medical
Languages : en
Pages : 164
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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
Get Book Here
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 Everett Meehl
Publisher:
ISBN:
Category : Medical
Languages : en
Pages : 174
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Book Description
"This monograph is an expansion of lectures given in the years 1947-1950 to graduate colloquia at the universities of Chicago, Iowa, and Wisconsin, and of a lecture series delivered to staff and trainees at the Veterans Administration Mental Hygiene Clinic at Ft. Snelling, Minnesota. Perhaps a general remark in clarification of my own position is in order. Students in my class in clinical psychology have often reacted to the lectures on this topic as to a protective technique, complaining that I was biased either for or against statistics (or the clinician), depending mainly on where the student himself stood! This I have, of course, found very reassuring. One clinical student suggested that I tally the pro-con ratio for the list of honorific and derogatory adjectives in Chapter 1 (page 4), and the reader will discover that this unedited sample of my verbal behavior puts my bias squarely at the midline. The style and sequence of the paper reflect my own ambivalence and real puzzlement, and I have deliberately left the document in this discursive form to retain the flavor of the mental conflict that besets most of us who do clinical work but try to be scientists. I have read and heard too many rapid-fire, once-over-lightly "resolutions" of this controversy to aim at contributing another such. The thing is just not that simple. I was therefore not surprised to discover that the same sections which one reader finds obvious and over-elaborated, another singles out as especially useful for his particular difficulties. My thesis in a nutshell: "There is no convincing reason to assume that explicitly formalized mathematical rules and the clinician's creativity are equally suited for any given kind of task, or that their comparative effectiveness is the same for different tasks. Current clinical practice should be much more critically examined with this in mind than it has been"--Preface. (PsycINFO Database Record (c) 2006 APA, all rights reserved).
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309171148
Category : Medical
Languages : en
Pages : 221
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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: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 218
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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: Howard N. Garb
Publisher: Amer Psychological Assn
ISBN: 9781557984838
Category : Medical
Languages : en
Pages : 333
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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: Joseph Tal
Publisher: Academic Press
ISBN: 0123869099
Category : Mathematics
Languages : en
Pages : 279
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Book Description
Delineates the statistical building blocks and concepts of clinical trials.
Author: Hans van Houwelingen
Publisher: CRC Press
ISBN: 1439835438
Category : Mathematics
Languages : en
Pages : 250
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Book Description
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a
Author: Paul Everett Meehl
Publisher: U of Minnesota Press
ISBN: 1452907749
Category : Medical
Languages : en
Pages : 385
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Book Description
Author: Jerry Z. Muller
Publisher: Princeton University Press
ISBN: 0691191263
Category : Business & Economics
Languages : en
Pages : 245
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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
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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"--
