Discrete strategies of cancer post-treatment surveillance. Estimation and optimization problems.
We consider the cancer post-treatment surveillance to be represented by a discrete observation process with a non-zero false-negative rate. Using a simple stochastic model of cancer recurrence derived within the 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 nonparametric maximum likelihood estimator for the tumor latency time distribution. When designing an optimal strategy of post-treatment surveillance, 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 methods by analyzing data on breast cancer recurrence.
Tsodikov
A D
AD
Biostatistical Unit, Institut Curie, Paris, France.
Asselain
B
B
Fourque
A
A
Hoang
T
T
Yakovlev AYu
D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology / Q000628:therapy*; D005188:False Negative Reactions; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008433:Mathematics; D015233:Models, Statistical; D011336:Probability; D012008:Recurrence; D013997:Time Factors; D017211:Treatment Failure