Should you always weight observations by exposure in a Poisson/Rate GLM

Question

There are many great posts here on the importance of using an offset in a rate regression.

For example, if you are modeling the the propensity of murder in towns, using population of town $n_i$ and number of murders $y_i$, you might model as

$$\lambda_i = y_i / n_i = e^{\beta_0 + \beta_1 x_1 + ... + \beta_k x_k + \epsilon_i}$$

$$log(y_i / n_i) = \beta_0 + \beta_1 x_1 + ... + \beta_k x_k + \epsilon_i $$

$$log(y_i) - log(n_i) = \beta_0 + \beta_1 x_1 + ... + \beta_k x_k + \epsilon_i $$

$$log(y_i) = log(n_i) + \beta_0 + \beta_1 x_1 + ... + \beta_k x_k + \epsilon_i $$

The $log(n_i)$ term here would be an offset.

Now, you want to estimate $\beta_0$, $\beta_1$, .., $\beta_k$ using a GLM

In the GLM, do you want to also weight observations by $n_i$ in the variance matrix?

I would imagine that you do, since you would expect a small town, with population, say, 100, to have a higher variance in reported murders, per capita, then a large city. But maybe this is already factored into the GLM in the MLE process somewhere else?

What are weights in a binary glm and how to calculate them? has the following comment as a footnote, but I don't understand it:

Similar considerations apply to other count-based GLM families such as Poisson and negative binomial. You can only set the GLM prior weights for those families to a value other than 1 if you are willing to embrace a quasi-likelihood model.

In another comment a highly-ranked user mentions the need for weights as well: When to use an offset in a Poisson regression?

Answer 1

No, you don't want to weight observations by $n_i$. The weight is already taken care of. There are a couple of ways to think about this.

• This is (or could be, depending on assumptions) maximum likelihood, so you shouldn't be doing any ad hoc messing with weights
• the combined effect of a variance function $V(\mu)=\mu$ and an offset of $\log n_i$ and a log link is to multiply the variance by $\exp(\log n_i))=n_i$

You can use weights and a rate as the outcome instead of counts and an offset:

> data("leukemia",package="survival")
> coef(summary(glm(status~offset(log(time))+x,data=aml,family=quasipoisson)))
                 Estimate Std. Error  t value     Pr(>|t|)
(Intercept)    -4.1014620  0.4183594 -9.80368 2.738519e-09
xNonmaintained  0.9580938  0.5351672  1.79027 8.784025e-02
> coef(summary(glm(I(status/time)~x,weights=time,data=aml,family=quasipoisson)))
                 Estimate Std. Error   t value     Pr(>|t|)
(Intercept)    -4.1014620  0.4183589 -9.803692 2.738462e-09
xNonmaintained  0.9580938  0.5351672  1.790270 8.784024e-02

The offset is better in the sense that it can be a model for what you think is actually happening, but that's the only difference.