Survival Model Predictive Accuracy and ROC Curves
Clicks: 3
ID: 294588
2005
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.6
/100
3 views
2 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #164 of 238 articles by views in biometrics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 238 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Summary The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R 2 , commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time‐dependent accuracy summaries based on time‐specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time‐dependent sensitivity and specificity, and time‐dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
| Reference Key |
openalex_W2109242212
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Patrick J. Heagerty, Yingye Zheng |
| Journal | biometrics |
| Year | 2005 |
| DOI |
10.1111/j.0006-341x.2005.030814.x
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.