Background:
Statistical methods commonly used in survival analysis typically provide the
probability that the difference between groups is due to chance, but do not offer a reliable estimate of
the average survival time difference between groups (the difference between median survival time is
usually reported).
Objective:
We suggest a Maximum-Entropy estimator for the average Survival Time Difference
(MESTD) between groups.
Methods:
The estimator is based on the extra survival time, which should be added to each member of
the group, to produce the maximum entropy of the result (resulting in the groups becoming most similar).
The estimator is calculated only from time to event data, does not necessarily assume hazard proportionality
and provides the magnitude of the clinical differences between the groups.
Results:
Monte Carlo simulations show that, even at low sample numbers (much lower than the ones
needed to prove that the two groups are statistically different), the MESTD estimator is a reliable predictor
of the clinical differences between the groups, and therefore can be used to estimate from (low
sample numbers) preliminary data whether or not the large sample number experiment is worth pursuing.
Conclusion:
By providing a reasonable estimate for the efficacy of a treatment (e.g., for cancer) even
for low sample data, it might provide useful insight in testing new methods for treatment (for example,
for quick testing of multiple combinations of cancer drugs).
Discrete Survival Time Models: An Application on Marriage Duration