Abstract:
The baseline parameter vector in a discrete-time survival model is determined by the number of
time points. The larger the number of the time points, the higher the dimension of the baseline
parameter vector which often leads to biased maximum likelihood estimates. One of the ways
to overcome this problem is to use a simpler parametrization that contains fewer parameters. A
simulation approach was used to compare the accuracy of three variants of penalised regression
spline methods in smoothing the baseline hazard function. Root mean squared error (RMSE)
analysis suggests that generally all the smoothing methods performed better than the model
with a discrete baseline hazard function. No single smoothing method outperformed the other
smoothing methods. These methods were also applied to data on age at rst alcohol intake
in Thohoyandou. The results from real data application suggest that there were no signi cant
di erences amongst the estimated models. Consumption of other drugs, having a parent who
drinks, being a male and having been abused in life are associated with high chances of drinking
alcohol very early in life.
