RT Journal Article
SR Electronic
T1 On modelling relative risks for longitudinal binomial responses: implications from two dueling paradigms
JF General Psychiatry
JO Gen Psych
FD BMJ Publishing Group Ltd
SP e100977
DO 10.1136/gpsych-2022-100977
VO 36
IS 2
A1 Tuo Lin
A1 Rongzhe Zhao
A1 Shengjia Tu
A1 Hao Wu
A1 Hui Zhang
A1 Xin M Tu
YR 2023
UL http://gpsych.bmj.com/content/36/2/e100977.abstract
AB Although logistic regression is the most popular for modelling regression relationships with binary responses, many find relative risk (RR), or risk ratio, easier to interpret and prefer to use this measure of risk in regression analysis. Indeed, since Zou published his modified Poisson regression approach for modelling RR for cross-sectional data, his paper has been cited over 7 000 times, demonstrating the popularity of this alternative measure of risk in regression analysis involving binary responses. As longitudinal studies have become increasingly popular in clinical trials and observational studies, it is imperative to extend Zou’s approach for longitudinal data.The two most popular approaches for longitudinal data analysis are the generalised linear mixed-effects model (GLMM) and generalised estimating equations (GEE). However, the parametric GLMM cannot be used for the extension within the current context, because Zou’s approach treats the binary response as a Poisson variable, which is at odds with the Bernoulli distribution for the binary response. On the other hand, as it imposes no mathematical model on data distributions, the semiparametric GEE is coherent with Zou’s modified Poisson regression. In this paper, we develop a GEE-based longitudinal model for binary responses to provide inference about RR.