A Framework for Evaluating Predictive Models Using Synthetic Image Covariates and Longitudinal Data

We present a novel framework for synthesizing patient data with complex covariates (e.g., eye scans) paired with longitudinal observations (e.g., visual acuity over time), addressing privacy concerns in healthcare research. Our approach introduces controlled association in latent spaces generating each data modality, enabling the creation of complex covariate-longitudinal observation pairs. This framework facilitates the development of predictive models and provides openly available benchmarking datasets for healthcare research. We demonstrate our framework using optical coherence tomography (OCT) scans, though it is applicable across domains. Using 109,309 2D OCT scan slices, we trained an image generative model combining a variational autoencoder and a diffusion model. Longitudinal observations were simulated using a nonlinear mixed effect (NLME) model from a low-dimensional space of random effects. We generated 1.1M OCT scan slices paired with five sets of longitudinal observations at controlled association levels (100%, 50%, 10%, 5.26%, and 2% of between-subject variability). To assess the framework, we modeled synthetic longitudinal observations with another NLME model, computed empirical Bayes estimates of random effects, and trained a ResNet to predict these estimates from synthetic OCT scans. We then incorporated ResNet predictions into the NLME model for patient-individualized predictions. Prediction accuracy on withheld data declined as intended with reduced association between images and longitudinal measurements. Notably, in all but the 2% case, we achieved within 50% of the theoretical best possible prediction on withheld data, demonstrating our ability to detect even weak signals. This confirms the effectiveness of our framework in generating synthetic data with controlled levels of association, providing a valuable tool for healthcare research.