Comparisons of Modeling Approaches for Evaluating the Longitudinal Association in a Clustered Healthcare Intervention Study

Abstract

This paper addresses methodology issues related to evidence-based healthcare research, specifically when evaluating and analyzing the hospital practice environments (HPE) impacts on the patient health outcomes are conducted in longitudinal intervention survey studies. HPE include the spatially clustered hospital characteristics, including practice environment scale (PES) measures, hospital facilities, nursing staffing and nursing attributes. The longitudinal associations between HPE and patient smoking cessation counseling (SCC) activities, and patient heart failure (HF) outcomes are examined. Various longitudinal and hierarchical modeling are compared including linear mixed models with restricted maximum likelihood estimation, generalized estimating equations with quasi-likelihood estimation, hierarchical linear regression models with nonparametric generalized least squares estimations, and repeated ANOVA. Moreover, both pre-modeling including the items/dimension reduction issues for longitudinal item-response hospital survey data and post-modeling (the mediation analysis) are discussed and conducted. Results show some methodology and solution differences when including the spatial or temporal correlations of HPE simultaneously for examining the longitudinal effects of HPE on HF core outcome measures adjusted or potentially mediated by SCC and nurse staffing environmental variables. This may have implications and potential impact for healthcare decision-making. Patients can benefit from these research findings.