Literature DB >> 26910239

Prognosis research and risk of bias.

Gennaro D'Amico1, Giuseppe Malizia2, Mario D'Amico3.   

Abstract

The interest in prognosis research has been steadily growing during the past few decades because of its impact on clinical decision making. However, since the methodology of prognosis research is still incompletely defined, the quality of published prognosis studies is largely unsatisfactory. Seven major domain for risk of bias in prognosis research have been identified, including study participation, attrition, selection of candidate predictors, outcome definition, confounding factors, analysis, and interpretation of results. The methodology for performing prognostic studies is currently aimed at avoiding such potential biases. Amongst methodologic requirements in prognosis research, the following should be considered most relevant: beforehand publication of the study protocol including the full statistical plan; inclusion of patients at a similar point along the course of the disease; rationale and biological plausibility of candidate predictors; complete information; control of overfitting and underfitting; adequate data handling and analysis; publication of the original data. Validation and analysis of the impact that prediction models have on patient management, are key steps for translation of prognosis research into clinical practice. Finally, transparent reporting of prognostic studies is essential for assessing reliability, applicability and generalizability of study results, and recommendations are now available for this aim.

Entities:  

Keywords:  Impact analysis; Prediction rule; Prognosis research; Prognostic indicator; Prognostic model; Validation study

Mesh:

Year:  2016        PMID: 26910239     DOI: 10.1007/s11739-016-1404-z

Source DB:  PubMed          Journal:  Intern Emerg Med        ISSN: 1828-0447            Impact factor:   3.397


  37 in total

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Review 4.  Validation, updating and impact of clinical prediction rules: a review.

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Journal:  J Clin Epidemiol       Date:  2008-11       Impact factor: 6.437

Review 5.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

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Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

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Journal:  Ann Intern Med       Date:  1993-02-01       Impact factor: 25.391

Review 7.  A model to predict survival in patients with end-stage liver disease.

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Journal:  Hepatology       Date:  2001-02       Impact factor: 17.425

8.  Assessing the performance of prediction models: a framework for traditional and novel measures.

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Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

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Authors:  Sonia Ratib; Kate M Fleming; Colin J Crooks; Guruprasad P Aithal; Joe West
Journal:  J Hepatol       Date:  2013-10-12       Impact factor: 25.083

10.  Prognosis research strategy (PROGRESS) 4: stratified medicine research.

Authors:  Aroon D Hingorani; Daniëlle A van der Windt; Richard D Riley; Keith Abrams; Karel G M Moons; Ewout W Steyerberg; Sara Schroter; Willi Sauerbrei; Douglas G Altman; Harry Hemingway
Journal:  BMJ       Date:  2013-02-05
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Review 2.  Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools.

Authors:  João Apóstolo; Richard Cooke; Elzbieta Bobrowicz-Campos; Silvina Santana; Maura Marcucci; Antonio Cano; Miriam Vollenbroek-Hutten; Federico Germini; Carol Holland
Journal:  JBI Database System Rev Implement Rep       Date:  2017-04

3.  Association between Functional Performance and Return to Performance in High-Impact Sports after Lower Extremity Injury: A Systematic Review.

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Journal:  J Sports Sci Med       Date:  2020-08-13       Impact factor: 2.988

Review 4.  Outcomes in syncope research: a systematic review and critical appraisal.

Authors:  Monica Solbiati; Viviana Bozzano; Franca Barbic; Giovanni Casazza; Franca Dipaola; James V Quinn; Matthew J Reed; Robert S Sheldon; Win-Kuang Shen; Benjamin C Sun; Venkatesh Thiruganasambandamoorthy; Raffaello Furlan; Giorgio Costantino
Journal:  Intern Emerg Med       Date:  2018-01-18       Impact factor: 3.397

5.  Electronically Available Comorbidities Should Be Used in Surgical Site Infection Risk Adjustment.

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Journal:  Clin Infect Dis       Date:  2017-09-01       Impact factor: 9.079

Review 6.  Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

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Journal:  BMC Neurol       Date:  2018-03-09       Impact factor: 2.474

7.  Overinterpretation and misreporting of prognostic factor studies in oncology: a systematic review.

Authors:  Emmanuelle Kempf; Jennifer A de Beyer; Jonathan Cook; Jane Holmes; Seid Mohammed; Tri-Long Nguyên; Iveta Simera; Marialena Trivella; Douglas G Altman; Sally Hopewell; Karel G M Moons; Raphael Porcher; Johannes B Reitsma; Willi Sauerbrei; Gary S Collins
Journal:  Br J Cancer       Date:  2018-10-24       Impact factor: 7.640

  7 in total

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