The Meaning of Life…
Hello all, it’s EBP again,
I’m sure my title caught your attention, or at least made you stop and think “okay, who does this EBP think she is???”. But don’t be haters because I am going to talk about the meaning of life. In any case, I will move things along here and just say that the answer is “42”………….if you are still scratching your head, go to the nearest bookstore NOW and pick up your own copy of The Hitchhiker’s Guide to the Galaxy. It will be absolutely worth it, especially if you are convinced mice are a lot smarter than we think and you like depressed robots…….don’t question what I just wrote….just get the book.
Now that I’ve sucked you into my blog, I can now talk to you about my real “meaning of life” post: REGRESSION MODELS!!!!! woohoooo!!! I will first start out with simple terms, and next blog I will plan on continuing my talk on regression models building off of what was learned here.
So what is regression exactly?? Regression analysis is used to estimate the conditional expectation of a dependent variable (any outcome such as disease, no disease, death, no death, etc.) given the independent variables (such as gender, age, smoker, non-smoker, etc.). Depending on how many independent variables are involved in your study, you can have either a Simple Linear Regression or a Multivariable Linear Regression. These regression models do not only consist of linear terms, however. There are many other regression models to be aware of and which one to use will be dependent on the nature of your outcome or independent variable:
> when outcome variable is continuous- perform Linear Regression
>when outcome variable is categorical – perform Logistic Regression
>when outcome variable is levels/counts-perform Poisson Regression
>when outcome variable is time to event (aka time to disease or death)-perform Cox Regression
>in addition, when encompassing both continuous and categorical predictors/outcomes, GLM or a Generalized Linear Model (an extension of simple linear regression) should be performed
Understanding what your variables are is extremely important in order to choose the correct model to perform your analysis. For example, let’s look at choosing logistic regression as a model for an analysis. I would first have to verify that my outcome variable is categorical. I would also have to remind myself that performing this regression model will present me with odds ratios in the occurrence of the categorical outcome variable. Logistic regression can also be verified as the correct model for my analysis after identifying what type of study is being performed. Case-control studies, which are good for rare diseases, are used for calculating odds ratios (as opposed to cohort studies that calculate relative risks). Thus, if I know my study was a case-control study, then I should automatically know that logistic regression would be the correct model for this particular analysis.
Further, use of the particular regression model you use cannot stop here. There are many more factors to take into account to make sure your model runs smoothly, such as adjusting for confounding factors and effect modifiers to name a few. Next time around, I will visit these concepts and continue to expand on the complexity of “the meaning of life”…regression models.