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Activity Number:
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621
- Beyond Linear Regression: Nonlinear Association, Quantile Regression and Generalized Linear Models
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Type:
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Contributed
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Date/Time:
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Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #304865
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Presentation 1
Presentation 2
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Title:
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Estimating Disparities in Breast Cancer Mortality by Race and Ethnicity
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Author(s):
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Ronald Gangnon* and Chrstina Hunter Chapman and Jennifer Bird and Amy Trentham-Dietz
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Companies:
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University of Wisconsin and University of Michigan and University of Wisconsin and University of Wisconsin
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Keywords:
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disparity;
breast cancer;
logistic regression;
generalized additive model;
shrinkage;
penalization
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Abstract:
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Assessments of long-term changes in breast cancer disparities by race/ethnicity require estimates of breast cancer mortality (or incidence) by race/ethnicity for past years, but data is only available for many racial/ethnic groups (Asian/Pacific Islander, American Indian/Alaska Native, Hispanic) for relatively recent years. We propose an age-period-cohort (APC) modeling strategy that borrows strength from the overall (all races and ethnicities combined) APC model to stabilize the APC model estimates for racial/ethnic subgroups. Age, period (year of death) and cohort (year of birth) are entered into a generalized additive regression model as additive natural cubic splines. To stabilize estimates for the race- and ethnicity-specific models, differences between the component functions for all races/ethnicities combined and for each racial/ethnic subgroup will be penalized towards no effect. Smoothing parameters for the various splines will be selected using generalized cross-validation with a BIC-like penalty to avoid overfitting. We illustrate the approach by estimating disparities in the proportion of deaths due to breast cancer between racial/ethnic subgroups from 1965-2016.
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Authors who are presenting talks have a * after their name.
