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Discovering the causes of cancer and the means of prevention
 

Measuring the Mortality Reductions Produced by Organized Cancer Screening: A Principled Approach - Dr. Hanley

Biostatistics Branch Seminar Series

January 14, 2020 | 3:00 PM – 4:00 PM

NCI Shady Grove 6E032/034 Rockville, M.D.

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Speaker

James Hanley, Ph.D., Department of Epidemiology, 
Biostatistics and Occupational Health, McGill University

Abstract

In cancer screening trials and population-based comparisons, mortality reductions are usually summarized by an overall (single-number) mortality reduction. This proportional hazards model is logically untenable. I describe a model Liu et al. (IntStatRev2015) for the expected reductions in each (Age,Year) cell of a Lexis diagram. It describes the effect of one round of screening with 3 parameters (1) when in follow-up time the reduction produced by this one round is maximal (2) how large this reduction is and (3) how dispersed in follow-up time the reductions are. Using the screening history in each (Age,Year) cell as a design matrix, reductions from previous screens are combined.  Ultimately (if follow-up extends far enough beyond the last screen) the resulting hazard ratio curves  have bathtub shapes that are modulated by the screening histories. Used with follow-up-year-specific data, this model considerably refines the results in various screening trials. I illustrate it using data from screening trials in prostate, colon,  lung, and ovarian cancer, and -- for breast cancer -- using population data from Denmark (Njor, JMedScr2015) and Ireland. I show how analyses of such screening data can be extended/refined to incorporate the numbers and timing of screening invitations in relation to where in the Lexis diagram the deaths do/do not occur.


**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods.  The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**