If you are new to using generalized linear mixed effects models, or if you have heard of them but never used them, you might be wondering about the purpose of a GLMM. Mixed effects models are useful when we have data with more than one source of random variability. For example, an outcome may be measured more than once on the same person (repeated measures taken over time). When we do that we have to account for both within-person and across-person variability. A single measure of residual variance can’t account for both.

In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!). For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you. 

The Cox regression model has a fairly minimal set of assumptions, but how do you check those assumptions and what happens if those assumptions are not satisfied?

Parametric models for survival data don’t work well with the normal distribution. The distributions that work well for survival data include the exponential, Weibull, gamma, and lognormal distributions among others. These distributions give you a broad range of hazard functions...

Survival analysis isn't just a single model. It's a whole set of tests, graphs, and models that are all used in slightly different data and study design situations. Choosing the most appropriate model can be challenging. In this article I will describe the most common types of tests and models in survival analysis, how they differ, and some challenges to learning them.

In this webinar you will learn what these variables are, introduce the relationships between the Poisson, Bernoulli, Binomial, and Normal distributions, and see an example of how to actually set up the data and specify and interpret the logistic model for these kinds of variables.

There are two features of survival models. First is the process of measuring the time in a sample of people, animals, or machines until a specific event occurs. In fact, many people use the term “time to event analysis” or “event history analysis” instead of “survival analysis” to emphasize the broad range of areas where you can apply these techniques.

Every statistical model and hypothesis test has assumptions. And yes, if you’re going to use a statistical test, you need to check whether those assumptions are reasonable to whatever extent you can. Some assumptions are easier to check than others. Some are so obviously reasonable that you don’t need to do much to check them […]

You show this table in your PowerPoint presentation because you know your audience is expecting some statistics, though they don’t really understand them. You begin by explaining that the constant (_cons) represents the mean BMI of small frame women. You have now lost half of your audience because they have no idea why the constant represents small frame women. By the time you start explaining the interaction you have lost 95% of your audience.

In this webinar you will learn what these variables are, introduce the relationships between the Poisson, Bernoulli, Binomial, and Normal distributions, and see an example of how to actually set up the data and specify and interpret the logistic model for these kinds of variables.