Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials

Typical methods of analyzing data from clinical trials have shortcomings, notably comparisons of group means, use of change scores from pre- and post-treatment assessments, ignoring intervening assessments, and focusing on direct effects of treatment. A comparison of group means disregards the likelihood that individuals have different trajectories of change. Moreover, change scores ignore intervening assessments that may provide useful information about change. This paper compares results from traditional regression-based methods for analyzing data from a clinical trial (e.g., regression with change scores) with those of latent growth curve modeling (LGM). LGM is a method that uses structural equation modeling techniques to model individual change, assess treatment effects and the relationship among multiple outcomes simultaneously, and model measurement error. The consequence is more precise parameter estimates while using data from all available time points. Results demonstrate that LGM can yield stronger parameter estimates than the traditional regression-based approach and explain more variance in the outcome. In trials where there is a true effect, but it is non-significant or marginally significant using the traditional methods, LGM may provide evidence of this effect. Analysts are encouraged to consider LGM as an additional and informative tool for analyzing clinical trial or other longitudinal data.