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The extension of total gain (TG) statistic in survival models: properties and applications

Overview of attention for article published in BMC Medical Research Methodology, July 2015
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Title
The extension of total gain (TG) statistic in survival models: properties and applications
Published in
BMC Medical Research Methodology, July 2015
DOI 10.1186/s12874-015-0042-x
Pubmed ID
Authors

Babak Choodari-Oskooei, Patrick Royston, Mahesh K.B. Parmar

Abstract

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
United States 1 8%
Unknown 10 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 50%
Student > Ph. D. Student 3 25%
Student > Doctoral Student 1 8%
Professor 1 8%
Student > Postgraduate 1 8%
Other 0 0%
Readers by discipline Count As %
Medicine and Dentistry 6 50%
Mathematics 3 25%
Psychology 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 July 2015.
All research outputs
#3,736,842
of 5,307,787 outputs
Outputs from BMC Medical Research Methodology
#524
of 635 outputs
Outputs of similar age
#130,765
of 187,724 outputs
Outputs of similar age from BMC Medical Research Methodology
#12
of 13 outputs
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