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

chapter |2 pages

Time (months) Time (months)

chapter |1 pages

placebo prednisone Time (years)

chapter |10 pages

Event Time Event Time

chapter |6 pages

Subject 1 Subject 2 Time

chapter |42 pages

Time

chapter 0|11 pages

5 10 15 8642 864205 10 15

chapter |28 pages

SI RR Time (years)

chapter |13 pages

Patient−Specific Slopes

chapter |2 pages

Residuals vs Fitted Normal Q−Q Marginal Survival Marginal Cumulative Hazard

Fitted Values Theoretical Quantiles

chapter |3 pages

Fitted Values

chapter |3 pages

Survival Function of Cox−Snell Residuals

Cox−Snell Residuals

chapter |4 pages

Fitted Values Fitted Values

chapter |14 pages

Fitted Values

chapter 02468|7 pages

10

chapter 321002468|5 pages

10

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 1|13 pages

−Specificity

chapter 1|3 pages

− Specificity

(Follow−up time(s): 0, 0.2, 1, 3, 4) ∆t = 1.0 ∆t = 2.0 ∆t = 4.0

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