Combining Generalized Linear Mixed Modeling and Random Effects Modeling to Provide a Comprehensive Understanding of Individual Level Change in Probability of Outcome Over Time

• Activity Number: 544
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Christopher Pretz*
• Companies: Craig Hospital
• Keywords: Longitudinal Analysis ; Mixed Modeling ; Generalized Linear Mixed Modeling ; Random Effects Modeling

Abstract

Many different species of outcome are captured in longitudinal settings. In such settings it is often necessary to understand how the probability of an event changes over time at the individual level. To model time dependent probability of event data at the individual level the following approach is proposed. The first step is to utilize a generalized linear mixed model to estimate logits based on a temporally collected dichotomous response. Once a vector of logits is estimated for each individual, individual level profiles which represent temporal change can be generated. These profiles can then be modeled using random effects modeling upon which logits are subsequently transformed into probabilities. Once logits are transformed into probabilities, random effects modeling allows for understanding of how the probability of an event is not only related to time, but how the probability of an event is influenced by a series of individual level covariates. This presentation includes a number of examples using data from the Traumatic Brain Injury Model System National Database. A computer generated interactive tool is provided which displays results based on current research.