Journal ArticleDOI
Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions
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TLDR
A fifth level of evidence is proposed because it is believed that broad verification of a prediction rule's clinical impact is no less important than that of the prediction rule on which it is based, and progressive evidentiary standards emphasize that a prediction rules rises to the level of a decision rule only if clinicians use its predictions to help make decisions for patients.Abstract:
Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.read more
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BookDOI
Regression Modeling Strategies
TL;DR: Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas.
Journal ArticleDOI
Assessing the performance of prediction models: a framework for traditional and novel measures.
Ewout W. Steyerberg,Andrew J. Vickers,Nancy R. Cook,Thomas A. Gerds,Mithat Gonen,Nancy A Obuchowski,Michael J. Pencina,Michael W. Kattan +7 more
TL;DR: It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions.
Journal ArticleDOI
Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.
Karel G.M. Moons,Douglas G. Altman,Johannes B. Reitsma,John P. A. Ioannidis,Petra Macaskill,Ewout W. Steyerberg,Andrew J. Vickers,David F. Ransohoff,Gary S. Collins +8 more
TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
Journal ArticleDOI
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
TL;DR: The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used, and is best used in conjunction with the TRIPod explanation and elaboration document.
Journal ArticleDOI
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
TL;DR: The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in timethat is, the moment of prediction (T= 0), and prognostic prediction involves a longitudinal relationship.
References
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