Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures | Zendy

Raphael Sonabend | Zendy; Andreas Bender | Zendy; Sebastian J. Vollmer | Zendy

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Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures

Author(s) -

Raphael Sonabend,

Andreas Bender,

Sebastian J. Vollmer

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac451

Subject(s) - hacker , computer science , software , transparency (behavior) , code (set theory) , distribution (mathematics) , source code , machine learning , econometrics , artificial intelligence , data mining , mathematics , computer security , mathematical analysis , set (abstract data type) , programming language , operating system

In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear method to derive a risk prediction from a distribution prediction. We survey methods proposed in literature and software and consider their respective advantages and disadvantages.

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