International Biometric Society
Assessing the effect of a binary time-dependent covariate on patient survival using
generalised pseudo-values with application to the comparison of chemotherapy and
stem-cell transplantation
Ulrike Pötschger1,2, Harald Heinzl2, Maria Grazia Valsecchi3, Martina Mittlböck2
1 Children's Cancer Research Institute, Vienna, Austria
2 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University
of Vienna, Vienna, Austria
3 Center of Biostatistics for Clinical Epidemiology, Department of Health Sciences,
University of Milan-Bicocca, Monza, Italy
When comparing allogeneic stem-cell transplantation (SCT) with conventional
chemotherapy in paediatric oncology, non-proportional hazards, waiting time to
transplant, and a primary interest in long-term survival rates have to be considered
simultaneously. This is methodologically challenging and available statistical
methods show several limitations that may lead to wrong conclusions. Hence, novel
approaches are doubtlessly needed.
For time-fixed covariates, the pseudo-value regression technique provides an
attractive alternative to the Cox-model for modelling survival-time events. Survival
probabilities at pre-specified time points are evaluated with generalised linear
models. The approach can be useful also when modelling survival in the absence of
proportional hazards. The original pseudo-value approach is generalised to allow for
a binary non-reversible time-dependent covariate. Using modified pseudo-values,
expected survival probabilities of patients with and without transition to a nonterminal state are estimated and compared. Adjusted for waiting time to the nonterminal state, this new approach allows studying the impact of the intermediate
event (e.g. SCT) on the probability to reach the absorbing state (e.g. death) within a
certain time interval. A real dataset is used to illustrate the practical value of the
approach: the main clinical aim was to compare SCT to chemotherapy in order to
investigate whether long-term survival probabilities can be improved with this more
intense therapy.
A simulation study based on examples from the medical literature further investigates
its statistical properties. The simulations show that the parameters estimates are
unbiased for any considered scenario. Furthermore confidence intervals show a very
good coverage. These results suggest that clinically relevant but methodologically
difficult situations like the comparison of the binary, time-dependent SCT with
chemotherapy can be reasonably and reliably addressed with statistical standard
software.
International Biometric Conference, Florence, ITALY, 6 – 11 July 2014