Estimating Overdiagnosis in Cancer Screening Studies

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 DESCRIPTION (provided by applicant): Project Summary/Abstract Millions of US men and women undergo screening for early cancer detection every year. Overdiagnosis, or the detection by screening of cases that would never become clinically diagnosed, is now recognized as the greatest potential harm of screening. This presents an especially difficult clinical dilemma in breast cancer, where screening detects many in-situ tumors, and in prostate cancer, where latent cases are highly prevalent in older age groups. Overdiagnosed cases cannot be helped by treatment and overtreatment of these cases carries a high price in terms of patient morbidity and economic costs. Knowledge about overdiagnosis is critical for well-formed screening policies and for well-informed patient decision making. However, overdiagnosis depends on screening practices and personal factors and many published studies are biased or do not apply to populations that differ from those used for estimation. The objective of this work is to advance knowledge about how to validly estimate overdiagnosis and to provide concrete information about overdiagnosis associated with specific cancer screening settings so as to inform screening policy development and clinical decision making. In our first aim we will conduct a simulation study to identify acceptable approaches and key characteristics of valid overdiagnosis studies. Lessons from this aim will be applied in our second aim, which will adapt established population models to estimate overdiagnosis rates associated with breast and prostate cancer screening under different screening policies and for different population subgroups defined by patient and tumor characteristics. These estimates will be made available to policy makers and clinicians via our third aim, which will develop online calculators to present relevant and useful information about overdiagnosis to these groups. Our study team of statisticians, breast and prostate cancer modelers and clinicians includes experts in simulation modeling, prominent clinical researchers and leaders in policy development for breast and prostate cancer screening. This work will move the field forward to a point of greater consensus about how to estimate overdiagnosis and what to tell policy makers and clinicians who have been tasked with shared and informed decision making. The knowledge generated by this application will lead to sound screening policies and better- informed clinical decisions and should provide a valuable quantitative tool in the ongoing battle to improve the balance of benefit and harm associated with cancer screening.