Abstract: We develop a probability model for evaluating long-term effects due to regular screening. People who take part in cancer screening are divided into four mutually exclusive groups: True-early-detection, No-early-detection, Overdiagnosis, and Symptom-free-life. For each case, we derive the probability formula. Simulation studies using the HIP (Health Insurance Plan for Greater New York) breast cancer study’s data provide estimates for these probabilities and corresponding credible intervals. These probabilities change with a person’s age at study entry, screening frequency, screening sensitivity, and other parameters. We also allow human lifetime to be subject to a competing risk of death from other causes. The model can provide policy makers with important information regarding the distribution of individuals participating in a screening program who eventually fall into one of the four groups.

Key words and phrases: Overdiagnosis, sensitivity, sojourn time, symptom free life, transition probability, true early detection.