Last data update: May 13, 2024. (Total: 46773 publications since 2009)
Records 1-2 (of 2 Records) |
Query Trace: Katzoff M[original query] |
---|
Analytical challenges for emerging public health surveillance
Rolka H , Walker DW , English R , Katzoff MJ , Scogin G , Neuhaus E . MMWR Suppl 2012 61 (3) 35-40 The root of effective disease control and prevention is an informed understanding of the epidemiology of a particular disease based on sound scientific interpretation of evidence. Such evidence must frequently be transformed from raw data into consumable information before it can be used for making decisions, determining policy, and conducting programs. However, the work of building such evidence in public health practice--doing the right thing at the right time--is essentially hidden from view. Surveillance involves acquiring, analyzing, and interpreting data and information from several sources across various systems. Achieving the goals and objectives of surveillance investments requires attention to analytic requirements of such systems. The process requires computer programming, statistical reasoning, subject matter expertise, often modeling, and effective communication skills. |
A hierarchical Bayesian nonresponse model for two-way categorical data from small areas with uncertainty about ignorability
Nandram B , Katzoff M . Surv Methodol 2012 38 (1) 81-93 We study the problem of nonignorable nonresponse in a two dimensional contingency table which can be constructed for each of several small areas when there is both item and unit nonresponse. In general, the provision for both types of nonresponse with small areas introduces significant additional complexity in the estimation of model parameters. For this paper, we conceptualize the full data array for each area to consist of a table for complete data and three supplemental tables for missing row data, missing column data, and missing row and column data. For nonignorable nonresponse, the total cell probabilities are allowed to vary by area, cell and these three types of "missingness". The underlying cell probabilities (i.e., those which would apply if full classification were always possible) for each area are generated from a common distribution and their similarity across the areas is parametrically quantified. Our approach is an extension of the selection approach for nonignorable nonresponse investigated by Nandram and Choi (2002a, b) for binary data; this extension creates additional complexity because of the multivariate nature of the data coupled with the small area structure. As in that earlier work, the extension is an expansion model centered on an ignorable nonresponse model so that the total cell probability is dependent upon which of the categories is the response. Our investigation employs hierarchical Bayesian models and Markov chain Monte Carlo methods for posterior inference. The models and methods are illustrated with data from the third National Health and Nutrition Examination Survey. |
- Page last reviewed:Feb 1, 2024
- Page last updated:May 13, 2024
- Content source:
- Powered by CDC PHGKB Infrastructure