By contrast, the statistically-acquired approach relies on analytic techniques to uncover the “correct” number of trajectory groups from a dataset.7–12 Growth mixture models allow investigators to estimate these trajectories—which typically vary as a function of intercept (i.e., the initial level of alcohol use), slope (i.e., the magnitude and direction of change), and growth (the linearity or curvilinearity of growth across development)—and examine their distinct relations to predictors and outcomes. Unfortunately, different studies yield different numbers of trajectories with different intercepts and slopes. These problems are due to substantial differences between studies in their sample and methods, their power to detect small classes, their timing and the length of their development window, their number of measurement occasions, their measurement and coding of alcohol use, their application of growth mixture modeling techniques, and statistical overfitting. If discrete trajectory groups do exist in the population, they are not consistently identified across studies13–15 or even within the same study.16
