 When it comes to longitudinal data, what analysis methods are we using today? How can they be applied to clinical data? In this 2018 paper, Stura et al. look at, for example, repeated data from measuring patient reactions and behaviors to a therapy. yet when analyzing this type of data problems arise; "more robust statistical methods" are required. The authors combine several methods to develop a "numerical tool based on optimization methods coupled with interpolation techniques." They conclude that it provides several benefits, including output displayed as "a (continuous) growth curve, allowing the analysis of each growth function independently of the others. " |
