Assessing Predictive Discrimination Performance of Biomarkers in The Presence of Treatment-Induced Dependent Censoring

Author:

Zhang Cuihong12,Ning Jing34,Belle Steven H.56,Squires Robert H.789,Cai Jianwen1011,Li Ruosha12

Affiliation:

1. Department of Biostatistics and Data Science , Houston , USA

2. The University of Texas Health Science Center at Houston , Houston , USA

3. Department of Biostatistics , Houston , USA

4. The University of Texas MD Anderson Cancer Center , Houston , USA

5. Department of Epidemiology , Pittsburgh , USA

6. The University of Pittsburgh , Pittsburgh , USA

7. Department of Pediatrics , Pittsburgh , USA

8. The University of Pittsburgh and Children's Hospital of Pittsburgh , Pittsburgh , USA

9. University of Pittsburgh Medical Center , Pittsburgh , USA

10. Department of Biostatistics , Chapel Hill , USA

11. University of North Carolina at Chapel Hill , Chapel Hill , USA

Abstract

Abstract In medical studies, some therapeutic decisions could lead to dependent censoring for the survival outcome of interest. This is exemplified by a study of paediatric acute liver failure, where death was subject to dependent censoring due to liver transplantation. Existing methods for assessing the predictive performance of biomarkers often pose the independent censoring assumption and are thus not applicable. In this work, we propose to tackle the dependence between the failure event and dependent censoring event using auxiliary information in multiple longitudinal risk factors. We propose estimators of sensitivity, specificity and area under curve, to discern the predictive power of biomarkers for the failure event by removing the disturbance of dependent censoring. Point estimation and inferential procedures were developed by adopting the joint modelling framework. The proposed methods performed satisfactorily in extensive simulation studies. We applied them to examine the predictive value of various biomarkers and risk scores for mortality in the motivating example.

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference35 articles.

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1. Dynamic risk score modeling for multiple longitudinal risk factors and survival;Computational Statistics & Data Analysis;2024-01

2. Improved mortality prediction for pediatric acute liver failure using dynamic prediction strategy;Journal of Pediatric Gastroenterology and Nutrition;2023-12-12

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