  Survival analysis of right censored data arises often in many areas of
research including medical research. Effect of covariates (and their
interactions) on survival distribution can be studied through existing methods
which requires to pre-specify the functional form of the covariates including
their interactions. Survival trees offer relatively flexible approach when the
form of covariates' effects is unknown. Most of the currently available
survival tree construction techniques are not based on a formal test of
significance; however, recently proposed ctree algorithm (Hothorn et al., 2006)
uses permutation test for splitting decision that may be conservative at times.
We consider parameter instability test of statistical significance of
heterogeneity to guard against spurious findings of variation in covariates'
effect without being overly conservative. We have proposed SurvCART algorithm
to construct survival tree under conditional inference framework (Hothorn et
al., 2006) that selects splitting variable via parameter instability test and
subsequently finds the optimal split based on some maximally chosen statistic.
Notably, unlike the existing algorithms which focuses only on heterogeneity in
event time distribution, the proposed SurvCART algorithm can take splitting
decision based in censoring distribution as well along with heterogeneity in
event time distribution. The operating characteristics of parameter instability
test and comparative assessment of SurvCART algorithm were carried out via
simulation. Finally, SurvCART algorithm was applied to a real data setting. The
proposed method is fully implemented in R package LongCART available on CRAN.
