Literature DB >> 11122504

Validation, calibration, revision and combination of prognostic survival models.

H C van Houwelingen1.   

Abstract

The problem of assessing the validity and value of prognostic survival models presented in the literature for a particular population for which some data has been collected is discussed. Methods are sketched to perform validation through 'calibration', that is by embedding the literature model in a larger calibration model. This general approach is exemplified for x-year survival probabilities, Cox regression and general non-proportional hazards models. Some comments are made on basic structural changes to the model, described as 'revision'. Finally, general methods are discussed to combine models from different sources. The methods are illustrated with a model for non-Hodgkin's lymphoma validated on a Dutch data set. Copyright 2000 John Wiley & Sons, Ltd.

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Year:  2000        PMID: 11122504     DOI: 10.1002/1097-0258(20001230)19:24<3401::aid-sim554>3.0.co;2-2

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  70 in total

1.  Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium.

Authors:  Emanuela Scarpi; Marco Maltoni; Rosalba Miceli; Luigi Mariani; Augusto Caraceni; Dino Amadori; Oriana Nanni
Journal:  Oncologist       Date:  2011-10-31

2.  Prospective comparison of prognostic scores in palliative care cancer populations.

Authors:  Marco Maltoni; Emanuela Scarpi; Cristina Pittureri; Francesca Martini; Luigi Montanari; Elena Amaducci; Stefania Derni; Laura Fabbri; Marta Rosati; Dino Amadori; Oriana Nanni
Journal:  Oncologist       Date:  2012-02-29

3.  Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV.

Authors:  Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Ted Clay; Deborah J Rennie; Eric Vittinghoff; R Adams Dudley
Journal:  Chest       Date:  2009-04-10       Impact factor: 9.410

4.  Validation of prognostic indices using the frailty model.

Authors:  C Legrand; L Duchateau; P Janssen; V Ducrocq; R Sylvester
Journal:  Lifetime Data Anal       Date:  2008-07-11       Impact factor: 1.588

5.  Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring.

Authors:  Bríain Ó Hartaigh; Heidi Gransar; Tracy Callister; Leslee J Shaw; Joshua Schulman-Marcus; Wijnand J Stuijfzand; Valentina Valenti; Iksung Cho; Jackie Szymonifka; Fay Y Lin; Daniel S Berman; Hyuk-Jae Chang; James K Min
Journal:  JACC Cardiovasc Imaging       Date:  2017-06-14

6.  Admission of patients with severe and moderate traumatic brain injury to specialized ICU facilities: a search for triage criteria.

Authors:  Chantal W P M Hukkelhoven; Ewout W Steyerberg; J Dik F Habbema; Andrew I R Maas
Journal:  Intensive Care Med       Date:  2005-04-16       Impact factor: 17.440

7.  Testing for improvement in prediction model performance.

Authors:  Margaret Sullivan Pepe; Kathleen F Kerr; Gary Longton; Zheyu Wang
Journal:  Stat Med       Date:  2013-01-07       Impact factor: 2.373

8.  Extubation outcome after a successful spontaneous breathing trial: A multicenter validation of a 3-factor prediction model.

Authors:  Yang Liu; Y U Mu; Guo-Qiang Li; Xin Yu; Pei-Jun Li; Zhi-Qi Shen; Hao-Xun Wang; Lu-Qing Wei
Journal:  Exp Ther Med       Date:  2015-08-12       Impact factor: 2.447

Review 9.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

10.  Proteomic Biomarkers of Atherosclerosis.

Authors:  F Vivanco; L R Padial; V M Darde; F de la Cuesta; G Alvarez-Llamas; Natacha Diaz-Prieto; M G Barderas
Journal:  Biomark Insights       Date:  2008-03-12
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