Choodari-Oskooei, Babak

Parmar, Mahesh KB

Royston, Patrick

English

PubMed

Babak Choodari-Oskooei

Patrick Royston

Mahesh KB Parmar

Springer - Publisher Connector

ORAL PRESENTATION Open Access
A new measure of predictive ability for survival
models
Babak Choodari-Oskooei*, Patrick Royston, Mahesh KB Parmar
From Clinical Trials Methodology Conference 2011
Bristol, UK. 4-5 October 2011
In clinical research, an understanding of prognostic fac-
tors is important in the design and analysis of clinical
trials and retrospective reviews of clinical experience.
The results of prognostic factor studies are usually sum-
marized in the form of statistics resulting from statistical
significance testing, i.e. estimated parameters, confidence
intervals, and p-values. These statistics do not inform us
whether prognostic factor information will lead to sub-
stantial improvement in the prognostic assessment. Pre-
dictive ability measures can be used for this purpose
since they provide important information about the
practical significance of prognostic factors. R2-type
indexes are the most familiar forms of such measures in
survival models, but they all have limitations and none
is widely used.
Bura and Gastwirth (2001) [1] proposed a new predic-
tive ability measure, named total gain (TG), for a logistic
regression model. TG is based on the binary regression
quantile plot, otherwise known as the predictiveness
curve, which was first proposed by Copas (1999) [2]. Gu
and Pepe (2009) [3] showed that TG is related to the
ROC summary index, but it does not have the reported
shortcomings of the ROC index.
In this paper, we extend the proposed TG measure to
survival models and explore its properties using simula-
tions and real data. In survival models, the TG statistic
is a non-negative, unitless measure of the total cumula-
tive distance between the average survival probability, as
expressed by the Kaplan-Meier (KM) estimates of the
survival probability, at a fixed time point and the esti-
mated survival probabilities from a given model. Stan-
dardised TG ranges from 0 (no explanatory power) to 1
(‘perfect’ explanatory power).
In our simulation studies, we investigated the impact
of censoring, covariate distribution and influential obser-
vations on the measure. The results of our simulations
show that unlike most of the other R2-type predictive
ability measures, TG is independent of censoring and
follow-up time. TG also increases as the effect of a cov-
ariate increases, but it is adversely affected by the cate-
gorisation of continuous prognostic factors. Finally, we
applied TG to quantify the predictive ability of prognos-
tic models developed in several disease areas. On bal-
ance, although TG lacks the intuitive interpretation of
the explained variation measures, our results indicate
that the estimates of the measure are within the reason-
able range of the estimates of explained variation mea-
sures and can be recommended as an alternative
measure to quantify the predictive ability in survival
models.
Published: 13 December 2011
References
1. Bura E, Gastwirth JL: The binary regression quantile plot: assessing the
importance of predictors in binary regression visually. Biometrical Journal
2001, 43:5-21.
2. Copas J: The effectiveness of risk Scores: the logit rank plot. Applied
Statistics 1999, 48(2):165-183.
3. Gu W, Pepe MS: Measures to summarize and compare the predictive
capacity of markers. The International Journal of Biostatistics 2009, 5(1):1-47.
doi:10.1186/1745-6215-12-S1-A138
Cite this article as: Choodari-Oskooei et al.: A new measure of predictive
ability for survival models. Trials 2011 12(Suppl 1):A138.
* Correspondence: Babak.oskooei@ctu.mrc.ac.uk
MRC Clinical Trials Unit, London, WC2B 6NH, UK
Choodari-Oskooei et al. Trials 2011, 12(Suppl 1):A138
http://www.trialsjournal.com/content/12/S1/A138 TRIALS
© 2011 Choodari-Oskooei et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.


A new measure of predictive ability for survival models

Choodari-Oskooei Babak

Royston Patrick

Parmar Mahesh KB

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Trials

Gastwirth JL: The binary regression quantile plot: assessing the importance of predictors in binary regression visually.

Measures to summarize and compare the predictive capacity of markers. The International Journal of Biostatistics

The effectiveness of risk Scores: the logit rank plot. Applied Statistics

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A new measure of predictive ability for survival models

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