There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?
When a model has a binary outcome, one common effect size is a risk ratio. As a reminder, a risk ratio is simply a ratio of two probabilities. (The risk ratio is also called relative risk.)
Risk ratios are a bit trickier to interpret when they are less than one.
A predictor variable with a risk ratio of less than one is often labeled a “protective factor” (at least in Epidemiology). This can be confusing because in our typical understanding of those terms, it makes no sense that a risk be protective.
In a recent article, we reviewed the impact of removing the intercept from a regression model when the predictor variable is categorical. This month we’re going to talk about removing the intercept when the predictor variable is continuous.
Spoiler alert: You should never remove the intercept when a predictor variable is continuous.
Here’s why. (more…)
by Jeff Meyer
As mentioned in a previous post, there is a significant difference between truncated and censored data.
Truncated data eliminates observations from an analysis based on a maximum and/or minimum value for a variable.
Censored data has limits on the maximum and/or minimum value for a variable but includes all observations in the analysis.
As a result, the models for analysis of these data are different. (more…)
Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.
As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)
We had so many that although I spent about 40 minutes answering (more…)
Predictor variables in statistical models can be treated as either continuous or categorical.
Usually, this is a very straightforward decision.
Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.
Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.
But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.