Despite the advancements in decision-analytic models of AD, there remain several areas of improvement that are necessary to more appropriately and realistically capture the broad nature of AD and the potential benefits of treatments in future models of AD.

UKPDS-68 and 82) which may not be appropriate in type-1-diabetes (T1D). The IMS-CORE-Diabetes-Model (CDM) has recently been updated to include three novel CVD RPMs specific to T1D populations based on data from the Epidemiology-of-Diabetes-Interventions-and Complications-study (EDIC), the Pittsburgh-Epidemiology-of-Diabetes-Complications-Study (PEDC) and the Swedish-National-Diabetes-Register (SNDR). The objective of this study was to contrast model predictions for CVD incidence utilizing three T1D RPMs (EDIC, PEDC and SNDR) and compare those to published incidence from the EDIC study. Methods: The CDM was applied to project the incidence of myocardial-infarction (MI), stroke, heart-failure (HF) and ischemicheart-disease (IHD) utilizing three alternative CVD RPMs, the EDIC-RPM, PEDC-RPM and SNDR-RPM. The risk profile of a newly diagnosed T1D population (age 21 years, HbA1c 7%, systolic-blood-pressure 114 mmHg, body-mass-index 32 Kg/m2, highdensity-lipoprotein 45 mg/dl and total-cholesterol 170 mg/dl) was projected over 30 years. Total CVD was assessed as the sum of MI, IHD and stroke incidence to match the EDIC-CVD composite end point. Results: Applying the EDIC-RPM for a newly diagnosed T1D individual resulted in 30-year cumulative incidence of 4.84%, 0.74%, 4.93% and 0.73% for MI, stroke, IHD and HF, respectively. This compared 5.80%, 1.04%, 7.39% and 1.30% utilizing PEDC-RPM and 7.44%, 1.22%, 8.57% and 0.73% utilizing SNDR-RPM. The total composite cumulative incidence of predicted CVD was 10.50%, 14.23% and 17.23% for EDIC-RPM, PEDC-RPM and SNDR-RPM respectively, which compares to 8.70% CVD incidence observed during the EDIC study. ConClusions: The CDM closely reproduced the published EDIC-CVD incidence when using the EDIC-RPM. The higher CVD incidence estimated via PEDC-RPM and SNDR-RPM may be reflective of equations derived from routine clinical practice.

A85test dataset. The study estimated a 2014 national ≥ 18 aged HF prevalence of 2.31%, or 657,902 patients, which aligned well with literature estimates, 590,416 and 626,199 patients. When the model's prevalence estimates were stratified by patient age group, the majority differed by < 0.5% from literature prevalence estimates. ConClusions: The model's HF prevalence estimates closely match the literature both in overall and age stratified prevalence. As of 2014, approximately 2.31% of Canadians aged ≥ 18 are treated for HF. Overall, this study provides a mechanism to calculate detailed prevalence estimates in Canada using retail prescription data.

costs incurred by ADRD patients could have been prevented with better ambulatory care and effective treatment. Complex case management programs for ADRD patients should involve strategies to reduce PAHs in order to improve patient outcomes and lower costs.

treatment, prior to launch using clinical trial (CT) data and pre-launch observational data. Methods: This study utilized data from CTs for fingolimod, and administrative claims data from IMS PharMetrics Plus, 2007-2013 constructed between fingolimod (pre-launch) and other disease-modifying therapies (DMTs) given CT data and pre-launch observational data. We assessed the projected relapse probability (RP) of fingolimod, as if it were available to treat the entire pre-2010 RW MS population. This was achieved in a two-step modeling process: (1) we selected a population of MS patients as a reference population and characterized this population in the pre-2010 claims data by estimating the joint distribution of the covariates, and (2) we then modeled projections for the RP using CT data standardized by RW population characteristics. Using a machine learning platform, Reverse Engineering and Forward Simulation (REFSTM), we built an ensemble of predictive models to create weights that were used to standardize the CT population to the pre-2010 RW MS population. The method was further validated using post-2010 fingolimod claims data. Results: The RW MS population was older (7,471 patients; mean age= 42 years, SD= 8.6) than the CT population (243 fingolimod patients; mean age= 37 years, SD= 7.9). The projected RP in the RW setting was 0.12-0.24 and was similar to that in the CT setting (0.11-0.22). ConClusions: We developed a causal methodology that estimated the RP among MS patients treated with fingolimod from CT data by standardizing to a reference RW pre-launch population. Our E2E methodology is generalizable and thus proposes a framework for translation of intervention efficacy data into estimates of intervention effectiveness.