Abstract :
In their article, Dr. Janani et al. discussed some methods
to obtain adjusted risk ratio (RR).1
Among options, authors
mentioned the method named “expanded logistic regression”,
which consists in changing the original dataset by duplicating
data of each individual that developed the outcome.2,3 In this new
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The probability of success in the original dataset will be equal to
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instead of an odds ratio.
This simple tool could be useful for calculating adjusted RRs
even using not sophisticated software. The main problem with
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observed with the reference methods.4
It was suggested that robust standard errors (SE) are needed
to account for the within-subject correlation resulted from the
duplicated observations.1
However, robust estimation of SE
does not solve that problem because the dependence of duplicate
observations persists.
Recently, Dwivedia et al. proposed the cluster option to correct
Thus, each case and its
duplicate would be considered within a cluster, which allows
estimating RRs considering the dependence of these observations.
In order to represent the differences between robust estimation of
SE and cluster option for logistic regression, this communication
present an analysis comparing these two methods against logbinomial regression
