1. Clinical Epidemiology & Analytics – filling the evidence gap Woodie M. Zachry, III, PhD Global Lead Clinical Epidemiology and Analytics

2. 2Company Confidential © 2009 Abbott The Present – Overview of CE&A activities Establishing the disease profile –Natural history of the disease –Issues in special populations –Incidence/prevalence of the disease –Risk factors of disease Identifying drug safety issues in collaboration with Pharmacovigilance –Safety issues of Abbott products and other current therapies –Subpopulations at higher risk? –Drug-drug interactions? Providing clinical trial support and instrumentation –Identifying biomarkers/surrogate endpoints and its relationship to outcomes

3. 3Company Confidential © 2009 Abbott Study Types & Data Sources Study Type Potential Sources of Information Preclinical Database AEGISAERSWHORegistryClaims Data Clinical Trials Database LiteratureCochrane Systematic Review with Meta Analysis XX Randomized Controlled Trial XXX Experimental Designs XXX CohortXXX Case ControlXXX Case ReportXXXXXX

4. 4Company Confidential © 2009 Abbott GRADE –The Grading of Recommendations Assessment, Development and Evaluation (GRADE ) –Provides a system for rating quality of evidence and strength of recommendations that is explicit, comprehensive, transparent, and pragmatic and is increasingly being adopted by organizations worldwide High quality— Further research is very unlikely to change the estimate of effect Moderate quality— Further research is likely to have an important impact on the estimate of effect and may change the estimate Low quality— Further research is very likely to have an important impact on the estimate of effect and is likely to change the estimate Very low quality— Any estimate of effect is very uncertain

5. 5Company Confidential © 2009 Abbott Nonsystematic Clinical Experience Case-Control Case Series Observational Studies RCT Prospective RetrospectiveLess BiasMeta-analysis Uncontrolled Comparison with bias Less Bias Hierarchy of Evidence

6. 6Company Confidential © 2009 Abbott Multiple EBM Stakeholders Levels of Evidence Chest Users’ Guides JAMA ACP Journal Club Clinical Evidence Cochrane Collaborative CONSORT Statement RCTs QUORUM Statement Systematic Review Meta-Analysis Clinical Practice Guidelines EMEA HTAs FDA AHRQ NIH NICE

7. 7Company Confidential © 2009 Abbott Where we want to be Evidence Based Approach Evidence Summaries across All Phases of Development and Study Designs Identify Evidence Gaps and Propose Ways to Fill Gaps

8. Case-Control analysis of ambulance, emergency room, or inpatient hospital events for epilepsy and antiepileptic drug formulation changes Woodie M Zachry, III PhD Quynhchau D Doan PhD Jerry D Clewell, PharmD Brien J Smith MD

9. 9Company Confidential © 2009 Abbott Background Epilepsy Treatment Disease & Treatment –Incidence: 200,000 cases annually in US, Prevalence: 1% from birth to age 20, then 3% by age 75. 6 –Treatment choice dependent upon Partial vs. Generalized presentation, history & secondary causes. – “A-rated” compounds are considered to be therapeutically bioequivalent to the reference listed drug (United States Food & Drug Administration Center for Drug Evaluation & Research) Generic substitution, observational experience –65% of US physicians surveyed reported caring for a patient who had a breakthrough seizure after a brand to generic switch. 1 –49.2% of foreign physicians surveyed reported problems when switching from brand alternatives to generics. 2 –67.8% of surveyed neurologists reported breakthrough seizures after a switch. 3 –12.9% of Lamotrigine switches had to be switched back due to medical necessity (v.s 1.5-2.9 for Non-AED). 4 –10.8% of patients switching supplier for CBZ, PHT, & VAL had perceived problems validated by GP. 5 1.Berg MJ, Gross RA. Physicians and patients perceive that generic drug substitution of anti-epileptic drugs can cause breakthrough seizures - results from a U.S. survey. 60th Annual Meeting of the American Epilepsy Society; Dec 1-5, 2006; San Diego, California. 2.Kramer G. et al. Experience with generic drugs in epilepsy patients: an electronic survey of members of the German, Austrian and Swiss branches of the ILAE. Epilepsia 2007;48, 609-11. 3.Wilner AN. Therapeutic equivalency of generic antiepileptic drugs: results of a survey. Epilepsy Behavior 2004;5(6):995-8. 4.Andermann F, et al. Compulsory generic switching of antiepileptic drugs: high switchback rates ro branded compounds compared with other drug classes. Epilepsia 2007;48(3):464-9. 5.Crawford P. et al. Generic Prescribing for epilepsy. Is it safe? Siezure 1996;5:1-5. 6.Centers for Disease Control and Prevention 2007. http://www.cdc.gov/epilepsy/ Accessed October 10, 2007.http://www.cdc.gov/epilepsy/

10. 10Company Confidential © 2009 Abbott Confidence in Treatment-Effect Relationship Case ReportsCase-Control EpidemiologicalCohort EpidemiologicalRCCT Hypothesis generation Hypothesis test (without temporal relationship) Hypothesis test (with temporal relationship assessment) Hypothesis test (Cause – Effect relationship inferred) Spontaneous reports to authorities with variable completeness and data quality Subjects selected based on current disease status (yes / no). Retrospectively evaluate exposure to agent(s) & confounders Exposed Vs. non-exposed subjects assembled before development of disease. Baseline confounding variables assessed before disease development. Treatment and Control groups studied in randomized, blinded trial Detection bias Selection bias Effects of risk factors are most difficult to evaluate Confounding patient factors often not considered Cannot establish causality Usually not possible to calculate rate of development of disease given the presence or absence of exposure. 1,2 Cannot establish causality Treatment-emergent, temporal relationship to exposure, and incidence of disease can be measured. Most closely resembles RCT design. 1,2 Cannot establish causality Causality can be inferred Limited ability to detect rare events. Generalizability limited by inability to detect events in the greater population, and sub-populations. 1 Mednick D, Day D. JMCP 1997;3(1):66-75. 2 Hennekens, C. Epidemiology in Medicine. 3 Harris S. J Cont. Ed. In Health Prof 2000;20:133-45. Low High

11. 11Company Confidential © 2009 Abbott Methods Objective: To determine if patients who received epilepsy care in an inpatient setting, emergency room, or ambulance have greater odds of having had a change between A rated AED medication alternatives in the past 6 months when compared to epileptic patients with no evidence of receiving epileptic care in similar settings.

12. 12Company Confidential © 2009 Abbott Methods Retrospective claims database analysis utilizing the Ingenix LabRx database Case-control study –Unmatched & Matched 1:3 for age within 5 years and epilepsy diagnosis type –Index date for case patients: 1 st seizure event requiring inpatient admission, emergency room visit, or ambulance during 3Q2006 – 4Q2006 –Index date for control patients: 1 st office visit during 3Q2006 – 4Q2006 Index primary ICD-9 diagnosis of 345.xx excluding 345.6  12 and  64 years of age No inpatient admission, emergency room visit, or ambulance in 6 months prior to index date Possess at least 145 day supply of AED medication for 6 months prior to index event Continuous eligibility for 6 months prior to index.

13. 13Company Confidential © 2009 Abbott Diagnosis Categories Siezure type –Generalized Convulsive 345.0 Non-convulsive 345.1 Petite mal status 345.2 Grand mal status 345.3 –Partial Complex partial 345.4 Simple partial 345.5 Epilepsia partialis continua 345.7 –Other Other forms 345.8 Epilepsy unspecified 345.9 Modifier –XXX.X0 – without mention of intractable epilepsy –XXX.X1 – with mention of intractable epilepsy

14. 14Company Confidential © 2009 Abbott All Patients (Non-Matched) VariableCase Patients (n=417) Control Patients (n=5562) P value (a-priori=0.05) % Male44.8%45.1%NS Age (SD) Insurance Commercial Medicaid US Region West Midwest South Northeast 37.4yrs (14.8) 95.4% 4.6% 12.7% 33.1% 42.0% 12.2% 37.2yrs (14.6) 98.1% 1.9% 14.5% 33.8% 40.0% 11.6% NS <0.001 NS

15. 15Company Confidential © 2009 Abbott Matched Case-Control Patients VariableCase Patients (n=416) Control Patients (n=1248) P value (a-priori=0.05) % Male45.0%44.2%NS Age (SD) Insurance Commercial Medicaid US Region West Midwest South Northeast 37.4yrs (14.8) 95.4% 4.6% 12.7 % 33.2 % 41.8 % 12.3 % 37.5yrs (14.7) 98.2% 1.8% 14.3 % 33.6 % 39.2 % 13.0 % NS 0.004 NS

16. 16Company Confidential © 2009 Abbott All Patients (Non-Matched) Seizure TypeCase Patients (n=417) Control Patients (n=5562) Generalized nonintractable Generalized intractable Partial nonintractable Partial intractable Other, nonintractable Other, intractable 30.5% 9.1% 19.2% 26.4% 3.1% 11.8% 35.5% 6.7% 36.0% 16.6% 1.1% 4.1%  2 <0.001

17. 17Company Confidential © 2009 Abbott Matched Case-Control Patients Seizure TypeCase Patients (n=416) Control Patients (n=1248) Generalized nonintractable Generalized intractable Partial nonintractable Partial intractable Other, nonintractable Other, intractable 30.5% 9.1% 19.2% 26.4% 2.9% 11.8% 30.5% 9.1% 19.2% 26.4% 2.9% 11.8%  2 = NS

18. 18Company Confidential © 2009 Abbott All Patients (Non-Matched) Odds of a change between A rated alternatives Odds ratio = 1.915 (95% CI, 1.387 - 2.644) Patient switched medications Patients did NOT switch medications Case 47 370 Control3465216

19. 19Company Confidential © 2009 Abbott How to calculate an unmatched odds ratio

20. 20Company Confidential © 2009 Abbott Matched Case-Control Patients Odds of a change between A rated alternatives Odds ratio = 1.811 (95% CI, 1.247 – 2.629) Patient switched medications Patients did NOT switch medications Case47369 Control811167

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22. 22Company Confidential © 2009 Abbott Matched Case-Control Patients Excluding Medicaid Patients Odds of a change between A rated alternatives Odds ratio = 1.855 (95% CI, 1.262 – 2.726) Patient switched medications Patients did NOT switch medications Case45352 Control791146

23. 23Company Confidential © 2009 Abbott Matched Case-Control Patients Excluding Patients Who Changed Dosage Schedule Odds of a change between A rated alternatives Odds ratio = 2.011 (95% CI, 1.189 – 3.4) Patient switched medications Patients did NOT switch medications Case22205 Control49918

24. 24Company Confidential © 2009 Abbott Discussion This study tested a hypothesis and found a relationship between emergent and inpatient care visits and previous AED formulation switching. This is concordant with problems identified in the survey and case study literature. –surveyed physicians believe there may be potential safety problems associated with switching between AED formulations for the same medication –There is some evidence of a significant percentage of patients who must switch back to a branded formulation after trying a generic formulation.

25. 25Company Confidential © 2009 Abbott Discussion This study assumes that patients experiencing break-through seizures will seek care in emergency and inpatient settings more often than ambulatory settings. Study subjects seeking care for break through events in an ambulatory setting may have attenuated the true magnitude of the significant relationship found in this study. Attempts were made to strengthen the assumption that subjects were taking AEDs. However, claims data only records the date a prescription was filled, not when or if the patient took the medication. Subtle differences in formulations may take time to accumulate and effect outcomes. However, the majority of formulation changes occurred within 2 months of the index event.

26. 26Company Confidential © 2009 Abbott Discussion Several factors may play a role in break through seizures that were not controlled for in this analysis (e.g., sleep deprivation, alcohol intake, hormonal influences). These effects may be additive to or even supersede formulation changes in precipitating break-through seizures. Zonisamide became available as a generic during the study time period. The high percentage of zonisamide formulation changes may have played a role in the significant relationship discovered. Case-control studies cannot establish a temporal association between AED formulation switches and outcomes.

27. 27Company Confidential © 2009 Abbott Conclusions This analysis has found an association between patients who utilized an ER, ambulance or inpatient hospital for epilepsy and the prior occurrence of AED formulation switching involving “A” rated generics. –After matching by age and epilepsy diagnosis, Cases had 81% greater odds of prior “A” rated switches compared to matched controls. –The case population had significantly more Medicaid patients. –Post hoc analyses excluding patients who had a dosage change and Medicaid patients did not change the significance of the original analysis. –Further investigations are warranted to better understand a possible cause-effect relationship.

28. 28Company Confidential © 2009 Abbott

29. 29Company Confidential © 2009 Abbott Nonsystematic Clinical Experience Case-Control Case Series Observational Studies RCT Prospective RetrospectiveLess BiasMeta-analysis Uncontrolled Comparison with bias Less Bias Hierarchy of Evidence