Adolescence is characterised by rapid growth in height and changes in body composition. These growth patterns can be influenced by early life factors and have consequences for adult health. The SITAR (Super Imposition by Translation and Rotation) method of growth curve analysis summarises individual growth patterns using three parameters (size, timing, and intensity) that are estimated as random effects, plus a cubic spline estimate of the average growth curve. SITAR was designed to simplify the analysis of adolescent height growth curves in individuals and it explains over 95% of the age-specific variance in height, making it an effective summary of individual growth patterns. However, SITAR assumes a plateau or constant growth at the end of the growth spurt which means it fails to properly fit outcomes whose growth continues into adulthood (e.g., weight, adiposity, lean mass and bone mass) and as such its use beyond height remains limited. SITAR also depends on arbitrary selection of the number and spacing of knots in the cubic spline which makes it susceptible to overfitting and confirmation bias, and it uses older (slower) software to fit models. SITAR random effects can be related to earlier growth-affecting exposures or later health outcomes making it relevant for translational medicine and life course epidemiology however, these analyses are often performed in two-stages which can lead to bias due to underestimated standard errors. Lastly, to overcome data sharing challenges, international consortia are increasingly turning to privacy-preserving software that can facilitate remote multicohort research, with the DataSHIELD platform, one of the most widely used software, however, DataSHIELD currently lacks implementation of SITAR. The aim of this project is improve the generalisability, reproducibility, and efficiency of SITAR and to empower researchers with essential information and tools for the best-practice analysis of individual growth patterns and their determinants and outcomes. The project will address the current limitations described above by tackling the outstanding methodological issues, creating R software to implement the new insights and developing resources to guide researchers through their analyses. Methodological developments will include generalising SITAR to allow it to accurately fit weight, adiposity, lean mass, and bone mass, approaches to fit SITAR models in more efficient software, implementing P-splines as alternatives to estimate of the average growth curve, approaches to obtain unbiased standard errors when relating growth curve features to exposures or outcomes, and DataSHIELD modules to implement SITAR. Methods will be tested using repeated data from four prospective cohort studies from the UK, USA, and Canada, and simulation studies. An R library, workshop, and interactive guidance will enable statisticians and epidemiologists to apply the method relatively simply.