1
Kaplan-Meier method, competing risk models and Cox proportional hazard models were used.
2
These findings were robust to multiple sensitivity analyses and competing risk analysis.
3
Competing risks methodology was used to estimate and compare cause-specific cumulative incidence.
4
Survival estimates were derived using Cox regression and competing risk models.
5
Cox proportional hazards model for competing risks were used for analyses.
6
Competing risks methods were used to estimate the cumulative incidence of local failure.
7
A multivariable analysis was conducted using competing risk regression in the prediction of PCSM.
8
Cumulative incidence (CumI) of events was estimated in a competing risks framework.
9
Probability of recurrence was calculated using the competing risks method.
10
The cumulative risk of CNS progression was calculated using death as a competing risk.
11
The predicted risk of developing diabetes was described as sub-hazard ratios using competing risk regression.
12
OS was analysed using the Kaplan-Meier method and RFS was assessed using competing risk analysis.
13
Competing risk analysis and proportional hazards models were used to examine LR, DR, and OS.
14
Results were robust to various sensitivity analyses such as competing risk analysis and multiple imputation.
15
Algorithms will be developed using competing risks survival analysis.
16
Competing risk models assessed 90-day cumulative incidence of deintensification.