The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in one year to predict disease progression over eight years.
To model OA progression, we used eight-year joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire and clustered these functional data using a mixed-effects mixture model. We included 1243 subjects who at enrollment were classified as being at high risk of developing OA based on age, body mass index, and medical and occupational histories. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection operator (LASSO) regression models for predicting the probabilities of belonging to each cluster.
This project is currently under review:
Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. "Modeling and Predicting Osteoarthritis Progression: Data from the Osteoarthritis Initiative."
The source code can be found here:
Statistical modeling was based on the following papers:
James GM, Sugar CA. Clustering for Sparsely Sampled Functional Data. J Am Stat Assoc. 2003;98(462):397-408.
James GM, Hastie TJ. Functional linear discriminant analysis for irregularly sampled curves. J R Stat Soc Ser B Stat Methodol. 2001;63(3):533-550.
James GM, Hastie TJ, Sugar CA. Principal component models for sparse functional data. Biometrika. 2000;87(3):587-602.