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

Author Cannon, M. J. Google Scholar
Warner, L. Google Scholar
Taddei, J. A. Google Scholar
Kleinbaum, D. G. Google Scholar
Institution Emory Univ
Universidade Federal de São Paulo (UNIFESP)
Abstract 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.
Language English
Date 2001-05-15
Published in Statistics in Medicine. W Sussex: John Wiley & Sons Ltd, v. 20, n. 9-10, p. 1461-1467, 2001.
ISSN 0277-6715 (Sherpa/Romeo, impact factor)
Publisher Wiley-Blackwell
Extent 1461-1467
Origin http://dx.doi.org/10.1002/sim.682
Access rights Closed access
Type Article
Web of Science ID WOS:000168513900016
URI http://repositorio.unifesp.br/handle/11600/26554

Show full item record




File

File Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Search


Browse

Statistics

My Account