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The SHARE study contains health, lifestyle, and socioeconomic data from individuals ages 50 and older in European countries collected over several waves. Leveraging these data for research purposes can be daunting due to the complex structure of the longitudinal design. The two aims of our study are (1) to develop a framework and R code for data management of the SHARE data to prepare for data analysis, and (2) to demonstrate how to apply the framework to a specific research question, where the aim is to model the presence of clinically significant depression assessed by the 12-item Europe depression scale. The result is a framework that substantially reduces the time to initiate research studies using SHARE data, facilitating the data extraction, data preparation and initial data analysis, with reproducible R code. Further, we illustrate the extensive work required to prepare an analysis-ready data set to ensure the validity of the modeling results. This underlines the importance of carefully considering and recording data management decisions that have to be built into the research process. The results about sex differences in the probability of depression are consistent with previous literature. Our findings about age-associated changes can be opportunities for adequate treatment interventions.

Organizing and Analyzing Data from the SHARE Study with an Application to Age and Sex Differences in Depressive Symptoms