Towards cross‐cohort estimation of cognitive decline in neurodegenerative diseases: Biomarkers (non‐neuroimaging) / Method development and/or quality control.

Background: Heterogeneity of cohorts, in terms of inclusion criteria, design of follow‐up visits and batteries of cognitive assessments, hinders any thorough comparisons between them. For that reason, we build a cross‐cohort model of cognitive decline that can be personalized to any patient, allowing to impute partially or totally missing scores. This enables to compare the subjects' progression across cohorts thanks to common cognitive assessments. Method: Based on a generic framework of disease progression that handles longitudinal data (Schiratti et al. 2015, NIPS) and implemented in the Leaspy Python package, we first estimate the joint progression of a large set of cognitive scores from the ADNI database along a common disease timeline. This group‐average progression is then personalized to individual trajectories of patients from the PharmaCog and AIBL cohorts. In particular, all individuals are characterized by a relative pace of progression and onset age that can be compared across cohorts. To validate our model, we impute missing values of a given score, based on its past and future values together with the dynamics of concurrent scores and compare errors to constant predictions and linear regressions. We finally go further by simulating purposely concealed scores thanks to the progression of the remaining features so to mock reconstruction of scores that are entirely missing in a particular cohort. Results: Figure 1 shows the relative positioning of all subjects from the 3 cohorts based on their individual pace of progression and their age at disease onset. Such characterization enables to impute missing values of the MMSE or the Immediate/Delayed Logical Memory better than imputing the last known value or fitting a linear regression [Figure 2]. Furthermore, from the multiple measurements of a single score, the model is able to reconstruct the individual progression of the other scores at the corresponding visits [Figure 3], with a controlled error rate. Conclusion: Our model enables comparison of patients trajectories from different cohorts. It permits to lessen imputation errors of most cognitive scores in new cohorts and demonstrates a new way to simulate, at any individual visit, scores that have not been measured in practice. [ABSTRACT FROM AUTHOR]