2015-11-6

Donfeng Wu, Ph.D., Associate Professor, Department of Bioinformatics and Biostatistics

"Inference of Long Term Outcomes and Over-diagnosis in Periodic Cancer Screening"

We develop a probability model for evaluating long-term effects due to regular screening. Initially asymptomatic people who take part in cancer screening were categorized into four mutually exclusive groups: symptom-free-life, no-early-detection, true-early-detection, and over-diagnosis. For each case, we derive the probability formulae rigorously. 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, future screening frequency, and three key parameters (screening sensitivity, sojourn time and transition probability). Human lifetime is used as a competing risk of death from other causes by using the actuarial life table from the US Social Security Administration. Simulation studies show that the probability of over diagnosis relies on the sojourn time more than any other factors. The percentage of over diagnosis could be as high as 20-40% when the mean sojourn time changes from 10-20 years. However, for breast cancer, since the sojourn time is usually short, the possibility of over-diagnosis is not a big issue among the screen-detected cases, and it is about 4-7%. The model provides policy makers with important information regarding the distribution of individuals participating in a screening program who eventually fall into one of the four groups.

Stay connected TwitterFacebookLinkedInYouTubeInstagram