I am interested in estimating an adjusted risk ratio, analogous to how one estimates an adjusted odds ratio using logistic regression. Some literature (e.g., this) indicates that using Poisson regression with Huber-White standard errors is a model-based way to do this

I have not found literature on how adjusting for continuous covariates affects this. The following simple simulation demonstrates that this issue is not so straightforward:

`arr <- function(BLR,RR,p,n,nr,ce) { B = rep(0,nr) for(i in 1:nr){ b <- runif(n)<p x <- rnorm(n) pr <- exp( log(BLR) + log(RR)*b + ce*x) y <- runif(n)<pr model <- glm(y ~ b + x, family=poisson) B[i] <- coef(model)[2] } return( mean( exp(B), na.rm=TRUE ) ) } set.seed(1234) arr(.3, 2, .5, 200, 100, 0) [1] 1.992103 arr(.3, 2, .5, 200, 100, .1) [1] 1.980366 arr(.3, 2, .5, 200, 100, 1) [1] 1.566326`

In this case, the true risk ratio is 2, which is recovered reliably when the covariate effect is small. But, when the covariate effect is large, this gets distorted. I assume this arises because the covariate effect can push up against the upper bound (1) and this contaminates the estimation.

I have looked but have not found any literature on adjusting for continuous covariates in adjusted risk ratio estimation. I am aware of the following posts on this site:

but they do not answer my question. Are there any papers on this? Are there any known cautions that should be exercised?

**Answer**

I don’t know if you still need an answer to this question, but I have a similar problem in which I’d like to use Poisson regression. In running your code, I found that if I set up the model as

```
model <- glm(y ~ b + x, family=binomial(logit)
```

rather than as your Poisson regression model, the same result occurs: the estimated OR is ~1.5 as ce approaches 1. So, I’m not sure that your example provides information on a possible problem with the use of Poisson regression for binary outcomes.

**Attribution***Source : Link , Question Author : Community , Answer Author : David F*