CHAPTER 20 Getting the Hint from Epidemiologic Inference 291

Chapter 20

Getting the Hint from

Epidemiologic Inference

I

n Parts  5 and  6, we describe different types of regression, such as ordinary

least-squares regression, logistic regression, Poisson regression, and survival

regression. In each kind of regression we cover, we describe a situation in which

you are performing multivariable or multivariate regression, which means you are

making a regression model with more than one independent variable. Those

chapters describe the mechanics of fitting these multivariable models, but they

don’t provide much guidance on which independent variables to choose to try to

put in the multivariable model.

The chapters in Parts 5 and 6 also discuss model-fitting, which means the act of

trying to refine your regression model so that it optimally fits your data. When you

have a lot of candidate independent variables (or candidate covariates), part of

model-fitting has to do with deciding which of these variables actually fit in the

model and should stay in, and which ones don’t fit and should be kicked out. Part

of what guides this decision-making process are the mechanics of modeling and

model-fitting. The other main part of what guides these decisions is the hypoth-

esis you are trying to answer with your model, which is the focus of this chapter.

IN THIS CHAPTER»

» ^{Choosing potential confounders for}

your regression model»

» ^{Using a modeling approach to}

develop a final model»

» ^{Adding interactions to the final}

model»

» ^{Interpreting the final model for}

causal inference