A Claims Database Approach to Evaluating Cardiovascular Safety of ADHD Medications A. J. Allen, M.D., Ph.D. Child Psychiatrist, Pharmacologist Global Medical Director – Strattera Eli Lilly and Company

Copyright © 2006 Eli Lilly and Company Background Both clinical trial data and spontaneous post-marketing adverse event reports have limitations Optimal features of a study to “fill the gap” No treatment bias or control for treatment bias Well controlled in other ways (population demographics, comorbid conditions, other treatments, etc.) Large numbers of patients (to detect rare events) Clinically relevant population (including more severely ill “real world” patients and patients on combinations of meds) Provide useful data in a timely fashion (don’t want to wait years for results) Doable (infrastructure exists, ethical, etc.) All study designs have strengths and limitations, so the results of any study need to be considered in light of other available data

Copyright © 2006 Eli Lilly and Company Claims Database Study: Introduction Study design: retrospective cohort study of adults using a large health claims database from a U.S. managed care population, with propensity score matched cohorts of ADHD medication initiators Includes several optimal features: Propensity score matching minimizes biases introduced by non-random treatment assignment Comparison of atomoxetine to comparator drugs and to a general population cohort provides greater context and insights on safety profile Large sample size (N ≈ 12,000 per cohort) Generalizable to the U.S. adult, insured population Focus on patient population at greatest risk for cardiovascular events: adults Existing data → timely results

Copyright © 2006 Eli Lilly and Company Claims Database Study: Objective and Outcomes Objective: to estimate and compare the incidence of cardiovascular and cerebrovascular outcomes among three cohorts of adult patients 1. Atomoxetine initiators 2. Similar patients who initiate other medications for ADHD (stimulants) 3. Age and gender-matched general population cohort Outcomes of interest: Defined through ICD-9 diagnostic and procedure codes and confirmed through clinical review of claims profiles. Cerebrovascular accident (CVA) Heart failure Transient ischemic attack (TIA) Hypertensive crisis Acute myocardial infarction (AMI) Mortality – all cause Cardiac arrhythmia

Copyright © 2006 Eli Lilly and Company Claims Database Study: Propensity Score Matching Treatment selection is non-random in an observational study Patients may be channeled into different treatments based on their demographics and baseline diagnoses, medications, etc. This is known as confounding by indication and may bias study results. Propensity score matching is an effort to balance the groups on a wide range of predictors of atomoxetine use prior to treatment selection Diagnoses, medication dispensing, healthcare utilization measures, etc. in each patient’s record in the 6 months prior to cohort entry. The goal is to have two cohorts that are very similar on a broad range of characteristics and that are balanced for the propensity to initiate atomoxetine. Cohort 1: atomoxetine initiators Cohort 2: very similar subjects that initiate stimulants Review of propensity score modeling: D’Agostino R. Statist Med 1998;17:2265.

Copyright © 2006 Eli Lilly and Company Payment Claims in 6 months prior to Baseline Visit % General Population (Age/Gender Matched) Before Matching*After Matching* % Stimulants % Atomoxetine % Stimulants % Atomoxetine ADHD Alcohol Abuse Hypertension Psychosis Seizures *Before/After Propensity Score Matching

Copyright © 2006 Eli Lilly and Company Claims Database Study: Limitations Adults, not children But adults at higher risk and probably more sensitive to pulse and BP effects Retrospective, not prospective Treatment not randomized and not blinded Propensity score matching is a powerful, but imperfect surrogate for randomization Match on factors determined from claims in the 6 months prior to baseline (so if something appeared 7 months prior to baseline, it would be missed) Can’t match on factors that are unknown, or not measured Not all patients can be matched Balance ability to match with desire to maintain a large sample Large sample compared to clinical trials, but limited by size of available databases and this limits statistical power Claims data limitations vs. clinical data (e.g., diagnosis in chart)

Copyright © 2006 Eli Lilly and Company Final thoughts All study designs have strengths and limitations, so the results of any study need to be considered in light of other available data. Retrospective cohort study of adults using a claims database and propensity score matching has several optimal features and will provide useful data in a timely fashion

Copyright © 2006 Eli Lilly and Company Additional Slides

Copyright © 2006 Eli Lilly and Company Claims Database Study: Analytic Plan Analytic plan: Propensity score modeling to match stimulant initiators to atomoxetine initiators As-treated (time-on-drug) analysis – rates per 1,000 person-years and relative risks using Poisson regression As-matched (intent-to-treat) analysis – rates per 1,000 person-years and cumulative hazards and hazard ratios using Kaplan-Meier estimation and Cox proportional hazards regression

Copyright © 2006 Eli Lilly and Company Group Demographics at Baseline Visit % General Population (Age/Gender Matched) Before Matching*After Matching* % Stimulants % Atomoxetine % Stimulants % Atomoxetine Female Age Age Age Age Age Age > *Before/After Propensity Score Matching

Copyright © 2006 Eli Lilly and Company Payment Claims in 6 months prior to Baseline Visit % General Population (Age/Gender Matched) Before Matching*After Matching* % Stimulants % Atomoxetine % Stimulants % Atomoxetine Drug Dependent Asthma Disorder of Lipid Metabolism Statins Dispensed Migraine Medication Dispensed *Before/After Propensity Score Matching