Analysis of correlated discrete observations: Background, examples and solutions
The goal of this paper is to highlight the use and interpretation of statistical techniques that account for correlation in epidemiological data. A conceptual statistical background is provided, and the main types of regression models for correlated data are highlighted. These models include margina...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2003
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/33120 |
| _version_ | 1855515142572212224 |
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| author | Schukken, Y.H. Grohn, Y.T. McDermott, B. McDermott, John J. |
| author_browse | Grohn, Y.T. McDermott, B. McDermott, John J. Schukken, Y.H. |
| author_facet | Schukken, Y.H. Grohn, Y.T. McDermott, B. McDermott, John J. |
| author_sort | Schukken, Y.H. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The goal of this paper is to highlight the use and interpretation of statistical techniques that account for correlation in epidemiological data. A conceptual statistical background is provided, and the main types of regression models for correlated data are highlighted. These models include marginal models, random effect models and transitional regression models. For each model type an example with data from the veterinary literature is provided. The examples are specifically used to highlight estimation procedures for parameters, and the interpretation of the estimated parameters. This paper emphasizes that statistical techniques and software to fit them are more widely available now, but that parameters have different interpretations in different model types. Consequently, we stress the importance of focusing on choosing the most appropriate model for the specific purpose of the analysis. |
| format | Journal Article |
| id | CGSpace33120 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2003 |
| publishDateRange | 2003 |
| publishDateSort | 2003 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace331202024-04-25T06:00:18Z Analysis of correlated discrete observations: Background, examples and solutions Schukken, Y.H. Grohn, Y.T. McDermott, B. McDermott, John J. epidemiology data statistical data statistical methods models The goal of this paper is to highlight the use and interpretation of statistical techniques that account for correlation in epidemiological data. A conceptual statistical background is provided, and the main types of regression models for correlated data are highlighted. These models include marginal models, random effect models and transitional regression models. For each model type an example with data from the veterinary literature is provided. The examples are specifically used to highlight estimation procedures for parameters, and the interpretation of the estimated parameters. This paper emphasizes that statistical techniques and software to fit them are more widely available now, but that parameters have different interpretations in different model types. Consequently, we stress the importance of focusing on choosing the most appropriate model for the specific purpose of the analysis. 2003-06 2013-07-03T05:26:05Z 2013-07-03T05:26:05Z Journal Article https://hdl.handle.net/10568/33120 en Limited Access Elsevier Preventive Veterinary Medicine;59(4): 223-240 |
| spellingShingle | epidemiology data statistical data statistical methods models Schukken, Y.H. Grohn, Y.T. McDermott, B. McDermott, John J. Analysis of correlated discrete observations: Background, examples and solutions |
| title | Analysis of correlated discrete observations: Background, examples and solutions |
| title_full | Analysis of correlated discrete observations: Background, examples and solutions |
| title_fullStr | Analysis of correlated discrete observations: Background, examples and solutions |
| title_full_unstemmed | Analysis of correlated discrete observations: Background, examples and solutions |
| title_short | Analysis of correlated discrete observations: Background, examples and solutions |
| title_sort | analysis of correlated discrete observations background examples and solutions |
| topic | epidemiology data statistical data statistical methods models |
| url | https://hdl.handle.net/10568/33120 |
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