JSM 2015 Online Program

Online Program Home
My Program

Abstract Details

Activity Number: 84
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing
Abstract #315729 View Presentation
Title: Multinomial Regression for Correlated Data Using the Bootstrap in R
Author(s): Jennifer Thompson* and Timothy Girard and Pratik Pandharipande and E. Wesley Ely and Rameela Chandrasekhar
Companies: Vanderbilt University and Vanderbilt University Medical Center and Vanderbilt University Medical Center and Vanderbilt University Medical Center and Vanderbilt University
Keywords: multinomial regression ; bootstrap ; R ; correlated data ; clustered data

Multinomial regression is used for circumstances where the outcome of interest is categorical and has no inherent order. In the case where data is correlated - for example, multiple records clustered by subject - ignoring this correlation can result in biased standard errors and invalid inferences. We present a clustered bootstrap method for multinomial regression in the case of multiple measurements per subject. This method estimates coefficients, odds ratios, predicted probabilities and all related confidence intervals in the presence of both main effects and interactions and of restricted cubic splines in covariates. We illustrate the method with an example from a population of critically ill patients which examines the association between a continuous exposure and the patient's mental status on the day following exposure measurement, and compare our results with the multgee package in R, which provides GEE methods for correlated nominal responses.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home