Latent Growth Curve Modeling in Health Outcomes Research: An Illustrative Study

Health outcomes research assesses and evaluates the result of health interventions, care, and therapy. Health outcomes research studies often use longitudinal data or repeated measures of health outcomes from interventions as dynamic measures. However, the traditional regression methods of analyzing such data are inherently limited in scope and flexibility. Latent growth models provide the needed flexibility and robustness for longitudinal data analysis. To illustrate the rationale and methodology of latent growth models in the context of outcomes research, the author used simulated hypothetical data of 357 hypertensive patients with controlled blood pressure attending a clinic with blood pressure measurements collected over four time points or waves (T1, T2, T3, and T4). The growth model was developed using structural equation modeling with Analysis of Moment Structures (AMOS) software. The study assumed that blood pressure control followed a linear trend. The results showed that the simulated model provided an adequate fit to the data. At the intraindividual level, the mean blood pressure measure (intercept) at the starting point was significant. The rate of change (slope or growth parameter) was positive and significant, indicating improvement of blood pressure measures from one time point to another. The correlation between the intercept and slope parameters was not substantial, implying that the initial measure did not necessarily change over time (consistency). To explain possible interindividual differences based on assumed significant variances of the error variances of intercept and slope parameters, the effect of a time-invariant variable-sex on the conditional model was explored. The use of latent growth curve modeling in health outcomes research can enrich the scope and quality of information obtained from longitudinal outcomes data. Reporting format, interpretation, and further implications for outcomes research are provided in the paper.