Two datasets will be used in the short course. They are below with brief descriptions.
DESCRIPTION AND DATA TO COME.
The data description below draws heavily from Goldsmith et al (2016):
Accelerometers have become an appealing alternative to self-report techniques for studying physical activity in observational studies and clinical trials, largely because of their relative objectivity. During observation periods, the devices measure electrical signals that are a proxy for acceleration. ``Activity counts” are then devised by summarizing the voltage signals across a short period known as an epoch; one-minute epochs are common. Because accelerometers can be worn comfortably and unobtrusively, they produce around-the-clock observations of many kinds of activity.
Study participants were recruited from 50 Head Start centers in northern Manhattan, the Bronx, and Brooklyn, in neighborhoods with high rates of pediatric asthma. We used a survey instrument to collect data on the child’s age, race, sex, asthma symptoms and other medical conditions, birth order and family-related factors, and features of the home environment. Field staff measured the child’s height, weight, and skin-fold thicknesses The staff then attached the accelerometer to the child’s non-dominant wrist with a hospital band. We obtain \(y_i(t)\) by averaging, for each \(t\) separately, across the six days of observation for each child. Additionally, we aggregate into 10 minute epochs.
For data confidentiality reasons, we will work with a simulated dataset. These data are simulated to mimic real data: they use realistic covariates, coefficients based on a fitted model, and residuals based on those in a full analysis. The activity data can be found here.