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Can a Poisson regression be used to estimate risk?
I will however make two notes: A Poisson regression is estimating often a rate, not a risk, and thus the effect estimate from it will often be noted as a rate ratio (mainly, in my mind, so you can still abbreviate it RR) or an incidence density ratio (IRR or IDR).
What are confidence intervals for geometric and logistic regressions?
In simulation studies, confidence intervals for the OR were 56–65% as wide (geometric model), 75–79% as wide (Poisson model), and 61–69% as wide (negative binomial model) as the corresponding interval from a logistic regression produced by dichotomizing the data.
How are the exponents of a Poisson regression equal to?
With the multiplicative Poisson model, the exponents of coefficients are equal to the incidence rate ratio (relative risk). These baseline relative risks give values relative to named covariates for the whole population.
When to use odds ratio or relative risk regression?
In the case of a rare outcome (i.e. the prevalence of the outcome is ≤ 10%) the odds ratio will approximate the risk ratio and relative risk regression is not necessary.
When to use odds ratios instead of binary response regression?
There is increasing interest in the public health community in estimating and drawing inferences about risk ratios and risk differences instead of odds ratios in the binary-response regression setting (e.g., see Greenland ( 1, 2 )).
How are generalized linear models used to estimate risk ratios?
Generalized linear models (GLMs) are often used with binary outcomes to estimate odds ratios. Though not as widely appreciated, GLMs can also be used to quantify risk differences, risk ratios, and their appropriate standard errors ( 1 ).
How is logistic regression used to estimate relative risk?
The logistic model can be used to estimate an odds ratio that, with sufficiently low disease prevalence, approximates a relative risk. Other regression techniques that directly estimate it, like binomial regression, have convergence issues that can easily derail a new student.