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Abstract Details
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Activity Number:
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187
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #301948 |
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Title:
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Applications of the K-th Year Moving Average Approach to Time Series Forecasting of Breast Cancer Mortality Rates
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Author(s):
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Michael Kotarinos*+ and Chris Tsokos
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Companies:
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University of South Florida and University of South Florida
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Address:
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1608 Allen's Ridge Drive North, Palm Harbor, FL, 34683,
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Keywords:
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ARIMA ;
Time Series Analysis ;
Breast Cancer ;
Moving Average
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Abstract:
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The object of the present study is to apply a newly developed time series forecasting technique to yearly breast cancer mortality rates to develop a statistical model that characterizes breast cancer mortality over time. Using SEER cancer data, we develop a K-th Year ARIMA model for breast cancer mortality that reveals how breast cancer develops over time. By analyzing this time series, we determine the effectiveness of strategic initiatives to combat breast cancer and predict future trends in breast cancer mortality. Using residual analysis, the quality and effectiveness of the models were evaluated in regards to their predictive power and applicability in comparison to standard regression models.
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Authors who are presenting talks have a * after their name.
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