What can go wrong when you assume that correlated data are independent: an illustration from the evaluation of a childhood health intervention in Brazil.
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.
Cannon
M J
MJ
Department of Epidemiology, Emory University, 1518 Clifton Road, N.E., Atlanta, GA 30322, USA. mrc7@cdc.gov
Warner
L
L
Taddei
J A
JA
Kleinbaum
D G
DG
D000367:Age Factors; D001938:Brazil; D002656:Child Day Care Centers; D002675:Child, Preschool; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008137:Longitudinal Studies; D008954:Models, Biological; D015596:Nutrition Assessment; D016017:Odds Ratio; D017063:Outcome Assessment, Health Care / Q000379:methods*; D013997:Time Factors; D019282:Wasting Syndrome / Q000453:epidemiology / Q000517:prevention & control