JUNIPER
PUBLISHERS-BIOSTATISTICS AND BIOMETRICS OPEN ACCESS JOURNAL
Be Wary of Using Poisson Regression to Estimate Risk and Relative Risk
Authored By Leigh Blizzard
Fitting a log binomial model to binary outcome data makes it possible to
estimate risk and relative risk for follow-up data, and prevalence and
prevalence ratios for cross-sectional data. However, the fitting
algorithm may fail to converge when the maximum likelihood solution is
on the boundary of the allowable parameter space. Some authorities
recommend switching to Poisson regression with robust standard errors to
approximate the coefficients of the log binomial model in those
circumstances. This solves the problem of non-convergence, but results
in errors in the coefficient estimates that may be substantial
particularly when the maximum fitted value is large. The paradox is that
the circumstances in which the modified Poisson approach is needed to
overcome estimation problems are the same circumstances when the error
in using it is greatest. We recommend that practitioners should be wary
of using modified Poisson regression to approximate risk and relative
risk.
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