What can go wrong when you assume that correlated data are independent: an illustration from the evaluation of a childhood health intervention in Brazil

What can go wrong when you assume that correlated data are independent: an illustration from the evaluation of a childhood health intervention in Brazil

Autor Cannon, M. J. Google Scholar
Warner, L. Google Scholar
Taddei, J. A. Google Scholar
Kleinbaum, D. G. Google Scholar
Instituição Emory Univ
Universidade Federal de São Paulo (UNIFESP)
Resumo The key analytical challenge presented by longitudinal data is that observations from one individual tend to be correlated. Although longitudinal data commonly occur in medicine and public health, the issue of correlation is sometimes ignored or avoided in the analysis. If longitudinal data are modelled using regression techniques that ignore correlation, biased estimates of regression parameter variances can occur. This bias can lead to invalid inferences regarding measures of effect such as odds ratios (OR) or risk ratios (RR). Using the example of a childhood health intervention in Brazil, we illustrate how ignoring correlation leads to incorrect conclusions about the effectiveness of the intervention. Copyright (C) 2001 John Wiley & Sons, Ltd.
Idioma Inglês
Data de publicação 2001-05-15
Publicado em Statistics in Medicine. W Sussex: John Wiley & Sons Ltd, v. 20, n. 9-10, p. 1461-1467, 2001.
ISSN 0277-6715 (Sherpa/Romeo, fator de impacto)
Publicador Wiley-Blackwell
Extensão 1461-1467
Fonte http://dx.doi.org/10.1002/sim.682
Direito de acesso Acesso restrito
Tipo Artigo
Web of Science WOS:000168513900016
Endereço permanente http://repositorio.unifesp.br/handle/11600/26554

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