What is the broken stick model?
The broken stick model describes a set of individual curves by a linear mixed model using second-order linear B-splines. The model can be used to
- smooth growth curves by a series of connected straight lines;
- align irregularly observed curves to a common age grid;
- create synthetic curves at a user-specified set of break ages;
- estimate the time-to-time correlation matrix;
- predict future observations.
The user specifies a set of break ages at which the straight lines connect. Each individual obtains an estimate at each break age, so the set of estimates of the individual form a smoothed version of the observed trajectory.
What are the main model assumptions?
The main assumptions of the broken stick model are:
- The development between the break ages follows a straight line;
- Broken stick estimates follow a common multivariate normal distribution;
In order to conform to the assumption of multivariate normality, the user may fit the broken stick model on suitably transformed data that yield the standard normal (\(Z\)) scale.
Why should I want to use the broken stick model?
Three unique features of the broken stick model are:
- Modular: Issues related to nonlinearities of the growth curves in the observed scale can be treated separately, i.e., outside the broken stick model;
- Local: A given data point will contribute only to the estimates corresponding to the closest break ages;
- Exportable: The broken stick model can be exported and reused for prediction for new data in alternative computing environments.
What is in the package?
The brokenstick package contains functions to fit,
predict and plot data. See the reference
page for an overview.
Acknowledgment
This work was supported by the Bill & Melinda Gates Foundation. The contents are the sole responsibility of the authors and may not necessarily represent the official views of the Bill & Melinda Gates Foundation or other agencies that may have supported the primary data studies used in the present study.