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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: 317, (120 Live, 197 On-demand)
Category: WALS - Wednesday Afternoon Lectures
Runtime: 01:03:57
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.
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NLM Title: Using risk models for breast cancer prevention [electronic resource] / Mitchell H. Gail.
Series: Wednesday afternoon lecture series
Author: Gail, Mitchell H.
National Institutes of Health (U.S.)
Publisher:
Other Title(s): Wednesday afternoon lecture series
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: Lectures
Webcasts
Download: To download this event, select one of the available bitrates:
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Caption Text: Download Caption File
NLM Classification: WP 870
NLM ID: 101604611
CIT Live ID: 12305
Permanent link: http://videocast.nih.gov/launch.asp?17823

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