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Activity Number: 298 - Model/Variable Selection and Model Evaluation
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304772 Presentation
Title: Comparing Strategies in Estimating Variance of Risk Ratios with Random Population Sizes
Author(s): Tracy Pondo* and Laura A Cooley
Companies: CDC and CDC
Keywords: variance estimation; combined data; sample survey; weighting; random offset; incidence rates

Poisson regression models with population size as offset terms are often used to model incidence rates and test for significant differences between subpopulations. However, in some situations the subpopulation sizes can only be estimated from complex survey samples resulting in random offset terms. Correspondingly, the incidence rates have two sources of random variation: the Poisson process that generates the disease incidence and the sampling error from the survey. In this paper, we examined and compared two strategies to obtain accurate estimation of the variance of such incidence rates using disease counts from the Active Bacterial Core Surveillance system and population counts from the National Health Interview Survey. The first strategy is based on a delta method applicable to functions of data vectors estimated from independent surveys. The second strategy involves generating random samples of the case and population counts based on mean and variance estimates from the surveillance and survey data. Both methods of estimating variance provide similar results.

Authors who are presenting talks have a * after their name.

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