%0 Conference Paper %1 Yang2005 %A Yang, B %D 2005 %J SUGI 30 Proceedings %K REPEATEDMEASURES SAS randomeffects %T Analyzing incomplete binary repeated measures data using SAS® %U http://www2.sas.com/proceedings/sugi30/197-30.pdf %X This paper presents a case study in longitudinal data analysis where the goal is to estimate the efficacy of a new drug for treatment of MDD. Data characteristic indicate:1. Subjects from different treatment groups drop out differentially across time.2. There are a high proportion of subjects who never experience any response.To overcome these challenges, we developed a logistic random-effects model with random intercepts. While the model is specified conditionally on subject random effect variable, we also draw inferences on population-averaged important to the assessment of the treatments’ efficacy in a population. Specifically, we present and describe using SAS Proc NLMIXED and %GLIMMIX macro to fit the logistic random effects model.

@inproceedings{Yang2005, abstract = {This paper presents a case study in longitudinal data analysis where the goal is to estimate the efficacy of a new drug for treatment of MDD. Data characteristic indicate:1. Subjects from different treatment groups drop out differentially across time.2. There are a high proportion of subjects who never experience any response.To overcome these challenges, we developed a logistic random-effects model with random intercepts. While the model is specified conditionally on subject random effect variable, we also draw inferences on population-averaged important to the assessment of the treatments’ efficacy in a population. Specifically, we present and describe using SAS Proc NLMIXED and %GLIMMIX macro to fit the logistic random effects model.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Yang, B}, biburl = {https://www.bibsonomy.org/bibtex/2569c8c1658e6b39ed6d213670b162ab2/jepcastel}, interhash = {cfd7d1c83b378f639ef0615ea798c060}, intrahash = {569c8c1658e6b39ed6d213670b162ab2}, journal = {SUGI 30 Proceedings}, keywords = {REPEATEDMEASURES SAS randomeffects}, note = {5400<m:linebreak></m:linebreak>Mixed models; Regressió logística; SAS; Dades longitudinals}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Analyzing incomplete binary repeated measures data using SAS®}, url = {http://www2.sas.com/proceedings/sugi30/197-30.pdf}, year = 2005 }