Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The present study focuses on a longitudinal application of the two-method planned missingness design. We provide evidence of the effectiveness of this design for fitting developmental data. Methodologically, we extend the framework for modeling an average treatment effect. Finally, we provide code and step-by-step instructions for how to analyze longitudinal, treatment effect data within these frameworks.