Theoretical and Methodological Imperatives in Longitudinal Healthcare Design and Research

It is a challenge in pursuing an integrative approach to healthcare outcomes research. This paper articulates the need for conducting longitudinal data analysis to gain theoretical support for causal inquiries, using parallel latent growth curve modeling with multilevel predictors. Furthermore, the availability of time constant and time-varying predictors in multi-wave study design and analysis is imperative in the healthcare investigation of trajectories in disease progression and prevention. The two-fold purpose of this paper is to address how the measurement of healthcare outcomes is quantified and used in longitudinal studies, and how the variability of outcomes can be analyzed by growth curve modeling with parallel change parameters. Future longitudinal studies should be based on evidence-based design and thorough deductive reasoning coupled with a sound and parsimonious theoretical framework.