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Dynamic Predictions with Time-Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking
- Publication Year :
- 2013
-
Abstract
- A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting it is of medical interest to optimally utilize the recorded information and provide medically-relevant summary measures, such as survival probabilities, that will aid in decision making. In this work we present and compare two statistical techniques that provide dynamically-updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time-to-event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.<br />Comment: 34 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1303.2797
- Subjects :
- Statistics - Applications
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1306.6479
- Document Type :
- Working Paper