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What can go wrong when you assume that correlated data are independent: an illustration from the evaluation of a childhood health intervention in Brazil by Michael J. Cannon; Lee Warner; J. Augusto Taddei; David G. Kleinbaum is a Mathematics article available to read on EtoBox.

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## 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 2001 © John Wiley & Sons, Ltd.

Who reads 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

Michael J. Cannon; Lee Warner; J. Augusto Taddei; David G. Kleinbaum

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2001

Language

EN

Field

Mathematics (Physical Sciences)