Latent growth models matched to research questions to answer questions about dynamics of change in multiple processes

Given theoretical and methodological advances that propose hypothesis about change in one or multiple processes, analytical methods for longitudinal data have been developed that provide researchers with various options for analyzing change over time. In this paper, we revisit several latent growth curve models that may be considered to answer questions about repeated measures of continuous variables, which may be operationalised as time varying covariates or outcomes.
Study design and setting
To illustrate each of the models discussed and how to interpret parameter estimates, we present examples of each method discussed using cognitive and blood pressure measures from a longitudinal study of ageing, the OCTO Twin Study.
Result and Conclusion
Although statistical models are helpful tools to test theoretical hypotheses about the dynamics between multiple processes, the choice of model and its specification will influence results and conclusions made.