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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Reporting methods in studies developing prognostic models in cancer: a review.

Susan Mallett¹, Patrick Royston, Susan Dutton

¹Centre for Statistics in Medicine, University of Oxford, Linton Rd, Oxford, UK. susan.mallett@csm.ox.ac.uk

|April 1, 2010

Summary

Prognostic models in cancer research often use flawed statistical methods and lack sufficient prospective data, compromising their reliability. Improved reporting and methods are crucial for accurate patient outcome predictions.

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Area of Science:

Oncology
Biostatistics
Medical Informatics

Background:

Prognostic models identify key variables for predicting patient outcomes.
Reliable models require robust statistical methods and existing disease knowledge.
Assessing the reporting and methods of cancer prognostic model development is essential.

Purpose of the Study:

To evaluate the reporting and statistical methods used in developing new cancer prognostic models.
To identify common deficiencies in the methodology of published prognostic models.

Main Methods:

Systematic literature search on PubMed for articles published in 2005.
Inclusion criteria: new cancer prognostic models, time-to-event outcomes, multivariable analysis, at least two variables.
Analysis of 47 selected articles.

Main Results:

Only 33% of studies used prospective data; 30% had insufficient events per variable (EPV).
Variable coding reported in 68%; recommended methods for continuous variables rarely used.
Flawed statistical methods, including inappropriate pre-screening for multivariate analysis (48% of studies), were common.

Conclusions:

Published cancer prognostic models frequently exhibit poor reporting and inappropriate multivariable modeling techniques.
Lack of prospective data and adequate sample size contribute to overfitting and limit model reliability.
Compromised reliability of prognostic models hinders accurate objective probability estimates for clinical decision-making.