Ad. Tsodikov et al., DISCRETE STRATEGIES OF CANCER POSTTREATMENT SURVEILLANCE - ESTIMATIONAND OPTIMIZATION PROBLEMS, Biometrics, 51(2), 1995, pp. 437-447
We consider the cancer post-treatment surveillance to be represented b
y a discrete observation process with a non-zero false-negative rate.
Using a simple stochastic model of cancer recurrence derived within th
e random minima framework, we obtain parametric estimates of both the
time-to-recurrence distribution and the probability of false-negative
diagnosis. Then assuming the false-negative rate known, we give a nonp
arametric maximum likelihood estimator for the tumor latency time dist
ribution. When designing an optimal strategy of post-treatment surveil
lance, we proceed from the minimum of the expected delay in detecting
tumor recurrence as a pertinent criterion of optimality. To solve this
problem we give a dynamic programming algorithm. We illustrate the me
thods by analyzing data on breast cancer recurrence.