Linear mixed effects methods for the analysis of longitudinal data provide a convenient framework for modelling within-individual correlation across time. Using spline functions allows for flexible modelling of the response as a smooth function of time. A computational connection between linear mixed effects modelling and spline smoothing has resulted in a cross-fertilization of these two fields. The connection has popularized the use of spline functions in longitudinal data analysis and the use of mixed effects software in smoothing analyses. However, care must be taken in exploiting this connection, as resulting estimates of the underlying population mean might not track the data well and associated standard errors might not reflect the true variability in the data. We discuss these shortcomings and suggest some easy-to-compute methods to eliminate them.

Additional Metadata
Keywords Linear mixed effects models, P-splines, Penalized smoothing, Sandwich estimator
Persistent URL dx.doi.org/10.1214/13-EJS809
Journal Electronic Journal of Statistics
Citation
Heckman, N. (Nancy), Lockhart, R. (Richard), & Nielsen, J. (2013). Penalized regression, mixed effects models and appropriate modelling. Electronic Journal of Statistics, 7(1), 1517–1552. doi:10.1214/13-EJS809