PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
PAGE 23 (2014) Abstr 3187 [www.page-meeting.org/?abstract=3187]
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Oral: Drug/Disease modelling
AL. Quartino (1), DG Polhamus (2), J Rogers (2), JY. Jin (1)
(1) Genentech Research and Early Development (gRED), Roche, South San Francisco, CA, USA (2) Metrum Research Group, Tariffville, CT, USA
Objectives: Progression in Alzheimer’s Disease (AD) manifests as changes in multiple biomarker, cognitive, and functional endpoints. The aim is to establish a natural disease progression model integrating multiple endpoints in patients with AD.
Methods: This analysis included 298 mild AD patients (baseline MMSE of 20-26) from the Alzheimer’s Disease Neuroimaging Initiative study . Longitudinal changes up to 3 years in the following nine endpoints were modeled: the 6 items of the CDR scale, ADAS-cog 12, hippocampal and ventricular volumes. The model for AD patients was based on a previous disease model for mild cognitive impaired patients  and fitted to the data using OpenBUGS v. 3.2.2.
Results: The developed model for mild AD patients linked the nine endpoints via a latent “disease status” variable that evolves linearly over time according to patient-specific rates, controlled by APOE4 status and baseline MMSE. The model correctly identifies healthier patients with slower progression rates with an increase of baseline MMSE, and more rapid disease progression in APoE4 positive patients. Residual likelihoods were best described by multinomial for CDR items; lognormal for hippocampal and ventricular volumes; and beta distributed for ADAS-cog (as when modeled in isolation ).
Simulations using the model resulted in relative changes in the natural disease progression over two years, with 90% credible intervals, as: ADAS-cog, 42.3 [35.9 – 49.9]%; CDR-sum, 93.3 [81.0 – 107.8]%; hippocampal volume, -7.6 [-8.8 – -6.6]%; and ventricular volume as 24.0 [21.4 – 26.9]%. These predictions are consistent with observed two year rates of progression in these patients; 38.5% in ADAS-cog, 85.2% in CDR sum of boxes, -7.8% in hippocampal volume, and 23.0% in ventricular volume, confirming the adequacy of the model.
Conclusions: We developed an integrated disease progression model, which quantifies the interaction between nine clinical and biomarker endpoints and covariates relevant to AD. Such models, compared to previous single endpoint disease models for AD, may provide greater insight of the impact of patient covariates and drug effect on disease as well as the sensitivity of the different endpoints in subpopulations. The model also permits trial simulation of multiple endpoints with an appropriate joint distribution; useful when multi-endpoint decision criteria are envisioned for analysis.