Estimating long-term progression from short-term data
Diseases that progress over long periods of time are often studied by observing cohorts at different stages of disease for shorter periods of time. We apply an Alternating Conditional Expectation (ACE) algorithm to estimate long-term multivariate monotone growth curves from short-term observations with unknown relative observation times. The iterative algorithm is demonstrated with the movies below.
Slides from AAIC 2012 are available here.
An R package to fit this model is available from https://bitbucket.org/mdonohue/grace
Alzheimer’s Disease Data (Amyloid+)
Fit
Disease Time Shifts
Residuals
Alzheimer’s Disease Data (Apoe \(\epsilon4+\))]
Fit
Disease Time Shifts
Residuals
Simulated data
Disease Time Shifts
Residuals
