Analysis of correlated discrete observations: background, examples and solutions. uri icon

abstract

  • 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
  • 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. (C) 2003 Elsevier Science B.V. All rights reserved.

publication date

  • 2003
  • 2003
  • 2003