Brief. Bioinformatics - On the validity of time-dependent AUC estimators.

Tópicos

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Resumo

Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied.

Resumo Limpo

recent develop molecular biolog led massiv discoveri new marker candid predict patient surviv evalu predict valu marker statist tool measur perform surviv model need consid estim discrimin measur popular approach evalu surviv predict biomark studi estim discrimin measur usual base regular assumpt proport hazard assumpt base two set molecular data simul studi show violat regular assumpt may lead overoptimist estim predict accuraci may therefor result bias conclus regard clinic util new biomark particular demonstr bias medic decis make possibl even statist check indic regular assumpt satisfi

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