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The parameter estimates of the base survival model are provided in Table 3, and could be estimated with good precision (RS E<23.1%).

A base parametric survival model was selected based on visual assessment of goodness of fit.

Covariate coefficients for age, primary tumour size, and number of nodes at diagnosis in the Cox regression used for the baseline predictive survival model were estimated based on a combined analysis of four Cancer and Leukemia Group B trials (Wood et al, 1985; Wood et al, 1994; Perloff et al, 1996; Parmigiani, 2002).

Survival model was performed using Cox proportional hazards.

Parametric regression survival models were based on maximum likelihood estimation.

All survival models were based on a monthly time scale and were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).

Survival models were evaluated based on visual assessment of predicted survival curves stratifying for covariate levels, and using the likelihood ratio test.

These analyses are based on empiric progression-free and overall survival data from the SATURN trial, and several parametric survival models were tested to identify the model with the best fit to the data.

Survival models were fitted using the Cox proportional hazard models, while survival curves were drawn based on the Kaplan-Meier methods.

Survival models were fitted using the Cox proportional hazard models, whereas survival curves were drawn based on the Kaplan Meier methods.

All survival models were checked for proportionality.