Recurrence-time model adjusted for time-varying incidence and sensitivity.

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Recurrence-time model adjusted for time-varying incidence and sensitivity.

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Screening is being widely implemented as a public health measure for many progressive diseases such as colon cancer, lung cancer and prostate and ovarian cancer, just to name a few. For programs of screening for cancer, a reduction in disease-specific mortality in the screened group is the main proof of benefit. There has been a long existing debate as to how to deal with biases that occur during the process of data analysis, such as lead-time bias, length bias and overdiagnosis. The focus of this work was initially to estimate and account for lead-time bias. In developing the model, it expanded to include estimated time-dependent sensitivity and age-specific preclinical incidence. To avoid biases, some investigators have used randomized trials of cancer screening, and compared mortality rates from the cancer under investigation between the screened and the control group, instead of comparing survival rates. Since a lot of data are collected in screening programs done outside the randomized trials, this work will focus on screening programs in “one-arm” studies, those for which randomization either does not exist or may be unethical. After preliminary discussions in Chapter I regarding screening models, including the concepts of sensitivity, specificity, positive and negative predictive values, and lead time, Chapter II presents an extension of recurrence-time models of periodic screening to accommodate time-varying incidence and sensitivity. Chapter III presents an application of the developed model to colon cancer screening data and Chapter IV is a summary and a conclusion of the entire work.


University of Minnesota Ph.D. dissertation. July 2011. Major: Environmental Health. Advisor: Prof. Dr. Timothy R. Church. 1 computer file (PDF); ix, 64 pages.

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Oancea, Sanda Cristina. (2011). Recurrence-time model adjusted for time-varying incidence and sensitivity.. Retrieved from the University Digital Conservancy,

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