ABSTRACT
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: Wit
TABLE OF CONTENTS
chapter |2 pages
Residuals vs Fitted Normal Q−Q Marginal Survival Marginal Cumulative Hazard
Fitted Values Theoretical Quantiles
chapter |1 pages
u = 1 u = 1.5u = 2 u = 3 u = 4 u = 5.5u = 6.5u = 7.9 u = 8.9u = 10.7 Predicted log serum bilirubin
jointFitBsp6.pbc jointFitBsp5.pbc jointFitBsp4.pbc jointFitBsp3.pbc jointFitBsp2.pbc
chapter |2 pages
u = 1 u = 1.5u = 2 u = 3 u = 4 u = 5.5u = 6.5u = 7.9 u = 8.9u = 10.7 Survival Probability
jointFitBsp6.pbc jointFitBsp5.pbc jointFitBsp4.pbc jointFitBsp3.pbc jointFitBsp2.pbc
chapter |27 pages
Case 1 − Specificity
(Follow−up time(s): 0, 0.2, 1, 3, 4) ∆t = 1.0 ∆t = 2.0 ∆t = 4.0