OBJECTIVE:Typical methods ofanalyzingdata fromclinicaltrialshave shortcomings, notably comparisons of group means, use ofchangescores 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 ofchange. Moreover,changescores ignore intervening assessments that may provide useful information aboutchange. This paper compares results from traditional regression-based methods foranalyzingdata from aclinicaltrial (e.g., regression withchangescores) with those oflatentgrowthcurvemodeling(LGM).METHODS:LGM is a method that uses structural equationmodelingtechniques to model individualchange, 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:Results demonstrate that LGM can yield stronger parameter estimates than the traditional regression-based approach and explain more variance in the outcome. Intrialswhere there is a true effect, but it is non-significant or marginally significant using the traditional methods, LGM may provide evidence of this effect.CONCLUSIONS:Analysts are encouraged to consider LGM as an additional and informative tool foranalyzingclinicaltrial or other longitudinal data.