Growth mixture models: a case example of the longitudinal analysis of patient‐reported outcomes data captured by a clinical registry

BMC Medical Research Methodology

Pamela A. Ratner¹  Richard Sawatzky²  Jae-Yung Kwon³  Jennifer Baumbusch³  Sandra Lauck³ 

[1] Department of Education and Counselling Psychology, and Special Education, Faculty of Education, University of British Columbia;School of Nursing, Trinity Western University;School of Nursing, University of British Columbia;

关键词: Growth mixture modelling;    Patient-reported outcomes;    Clinical registry;   

DOI  :  10.1186/s12874-021-01276-z

来源: DOAJ

【 摘 要 】

Abstract Background An assumption in many analyses of longitudinal patient-reported outcome (PRO) data is that there is a single population following a single health trajectory. One approach that may help researchers move beyond this traditional assumption, with its inherent limitations, is growth mixture modelling (GMM), which can identify and assess multiple unobserved trajectories of patients’ health outcomes. We describe the process that was undertaken for a GMM analysis of longitudinal PRO data captured by a clinical registry for outpatients with atrial fibrillation (AF). Methods This expository paper describes the modelling approach and some methodological issues that require particular attention, including (a) determining the metric of time, (b) specifying the GMMs, and (c) including predictors of membership in the identified latent classes (groups or subtypes of patients with distinct trajectories). An example is provided of a longitudinal analysis of PRO data (patients’ responses to the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire) collected between 2008 and 2016 for a population-based cardiac registry and deterministically linked with administrative health data. Results In determining the metric of time, multiple processes were required to ensure that “time” accounted for both the frequency and timing of the measurement occurrences in light of the variability in both the number of measures taken and the intervals between those measures. In specifying the GMM, convergence issues, a common problem that results in unreliable model estimates, required constrained parameter exploration techniques. For the identification of predictors of the latent classes, the 3-step (stepwise) approach was selected such that the addition of predictor variables did not change class membership itself. Conclusions GMM can be a valuable tool for classifying multiple unique PRO trajectories that have previously been unobserved in real-world applications; however, their use requires substantial transparency regarding the processes underlying model building as they can directly affect the results and therefore their interpretation.

【 授权许可】

Unknown