brokenstick - Broken Stick Model for Irregular Longitudinal Data
Data on multiple individuals through time are often
sampled at times that differ between persons. Irregular
observation times can severely complicate the statistical
analysis of the data. The broken stick model approximates each
subject’s trajectory by one or more connected line segments.
The times at which segments connect (breakpoints) are identical
for all subjects and under control of the user. A well-fitting
broken stick model effectively transforms individual
measurements made at irregular times into regular trajectories
with common observation times. Specification of the model
requires three variables: time, measurement and subject. The
model is a special case of the linear mixed model, with time as
a linear B-spline and subject as the grouping factor. The main
assumptions are: subjects are exchangeable, trajectories
between consecutive breakpoints are straight, random effects
follow a multivariate normal distribution, and unobserved data
are missing at random. The package contains functions for
fitting the broken stick model to data, for predicting curves
in new data and for plotting broken stick estimates. The
package supports two optimization methods, and includes options
to structure the variance-covariance matrix of the random
effects. The analyst may use the software to smooth growth
curves by a series of connected straight lines, to align
irregularly observed curves to a common time grid, to create
synthetic curves at a user-specified set of breakpoints, to
estimate the time-to-time correlation matrix and to predict
future observations. See <doi:10.18637/jss.v106.i07> for
additional documentation on background, methodology and
applications.