NIH VideoCast - Using Risk Models for Breast Cancer Prevention

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Using Risk Models for Breast Cancer Prevention

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Air date:	Wednesday, February 27, 2013, 3:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 716 (120 Live, 596 On-demand)
Category:	WALS - Wednesday Afternoon Lectures
Runtime:	01:03:57
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	Wednesday Afternoon Lecture Series Dr. Gail uses statistical methods for the design and analysis of epidemiologic studies including studies of genetic factors and models to predict the absolute risk of disease. Epidemiologic studies have established several risk factors for breast cancer such as family history, age when giving birth for the first time, biopsy findings, and mammographic density. These factors can be combined with breast cancer incidence rates to construct models of absolute risk of breast cancer. Absolute risk is the risk of developing a disease over a defined age interval or time period; relative risk is used to compare risks in different groups of people. Absolute risk is useful for counseling women and in public-health applications. In counseling, absolute risk estimates provide realistic perspectives and inform decision-making such as when or whether to begin having screening mammographies. Absolute risk estimates are also useful in weighing the risks and benefits, for example, of chemopreventive therapies such as tamoxifen or raloxifene. Public health applications of risk models include designing chemoprevention trials; implementing “high risk” prevention strategies that focus only on women who are at highest risk for breast cancer; assessing the potential of preventive interventions to reduce absolute breast cancer risk in the population; and using risk estimates to allocate prevention resources under cost constraints. Dr. Gail will review the usefulness of risk models in these applications and the potential of additional risk factors, such as single nucleotide polymorphisms, to improve the performance of those models.

NLM Title:	Using risk models for breast cancer prevention / Mitchell H. Gail.
Author:	Gail, Mitchell H. National Institutes of Health (U.S.)
Publisher:
Abstract:	(CIT): Wednesday Afternoon Lecture Series Dr. Gail uses statistical methods for the design and analysis of epidemiologic studies including studies of genetic factors and models to predict the absolute risk of disease. Epidemiologic studies have established several risk factors for breast cancer such as family history, age when giving birth for the first time, biopsy findings, and mammographic density. These factors can be combined with breast cancer incidence rates to construct models of absolute risk of breast cancer. Absolute risk is the risk of developing a disease over a defined age interval or time period; relative risk is used to compare risks in different groups of people. Absolute risk is useful for counseling women and in public-health applications. In counseling, absolute risk estimates provide realistic perspectives and inform decision-making such as when or whether to begin having screening mammographies. Absolute risk estimates are also useful in weighing the risks and benefits, for example, of chemopreventive therapies such as tamoxifen or raloxifene. Public health applications of risk models include designing chemoprevention trials; implementing "high risk" prevention strategies that focus only on women who are at highest risk for breast cancer; assessing the potential of preventive interventions to reduce absolute breast cancer risk in the population; and using risk estimates to allocate prevention resources under cost constraints. Dr. Gail will review the usefulness of risk models in these applications and the potential of additional risk factors, such as single nucleotide polymorphisms, to improve the performance of those models.
Subjects:	Breast Neoplasms--epidemiology Breast Neoplasms--prevention & control Models, Statistical Risk Assessment
Publication Types:	Lecture Webcast

NLM Classification:	WP 870
NLM ID:	101604611
CIT Live ID:	12305
Permanent link:	https://videocast.nih.gov/watch=12305

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