Tuning Epidemiological Study Design Methods for Exploratory Data Analysis in Real World Data Andrew Bate Senior Director, Epidemiology Group Lead, Analytics 15th Annual Meeting of the International Society of Pharmacovigilance, Prague, 30 October 2015
Disclosures and Acknowledgement • I am a full time employee of Pfizer and hold stocks and stock options • The work presented here represent a component of a multi-year internal to Pfizer methodology programme to test and better understand Active Surveillance approaches thanking the following for their contributions to the production of these results – Xiaofeng Zhou, Kathy Liu, Sundaresan Murugesan, Rongjun Shen, Richard Gong and Bing Cai • Desvenlafaxine (Pristiq) used for this study is a Pfizer product 2
Overview • The importance of assessing signal detection capability in longitudinal observational data • Current knowledge about signal capability in such data • Read out the results of quantitative signal detection testing on method testing focussing on – Adapting and assessing standard epidemiological approaches for signal detection – ‘Test set’ of more recently marketed products – Focussed detailed analysis of pre-defined small set of drug- outcome pairs 3
Increasingly Important to Harness the Power of Real World Evidence for Safety Characterize Patient Evaluate Product Risks Risk Profile Approval Active Surveillance Monitor and detect signals in defined Standing Cohorts patient cohorts using innovative analytic methods Post Approval Safety Studies Compare medication risks in the real world, as prescribed and taken during routine clinical practice Risk Minimization EMRs Evaluate the effectiveness of risk minimization measures (e.g., product Claims label/education) Registries 4
Good (but Imperfect) Method Performance Against Well Established Safety Reference Set Statistical UK EMR THIN Median across Method OMOP lab of 10 Comparison to OMOP RWA databases reference set of 53 well studied drug outcome Lower 95% Sensitivity Specificity Sensitivity Specificity pairs of established CI >1 drugs showed no single best method PRR 0.67 0.68 0.44 0.73 Generalizability to USCCS 0.78 0.59 0.44 0.68 NMEs? HDPS 0.50 0.76 0.56 0.82 • USCCS and HDPS are two very different analytic approaches extensively used by the epidemiological community– but with clearly different designs might be more effective for different drug-event pairs – Evaluated extensively for observational studies • PRR is a transparent approach and a key signal detection method for spontaneous reports - ideal reference method 5
Approach to Gain Insights Into Signal Detection Utility in Longitudinal Observational Data Resources for More Recently Marketed Products Drugs of Focus 5 Outcomes of Interest Databases UK EMR US Claims Inform understanding Active Surveillance of signal Algorithms detection method Lisinopril Versus Unexposed performance 80 60 70 Cumulative Adverse Events (angioedema) 50 60 Focused analysis 40 for recently 50 Relative Risk 40 30 30 20 marketed 20 10 10 0 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 products Months Observed AEs Expected AEs RR More clarity on how signal More understanding on detection on such data can add utility of different algorithms value compared to signals for different kinds of generated from other data outcomes sources externally 6
Reference Health Outcomes of Interest Used for Method Testing • The study focused on ability of methods to detect labelled outcomes • Selected outcomes – For Pristiq • Hypertension, Orthostatic hypotension, Proteinuria, Hyperlipidemia, All Fractures – For Humira • Acute Myocardial Infarction (AMI), GI Perforation, Herpes Zoster, Interstitial Lung Disease, Lymphoma, Pneumonia • A priori had varying levels of confidence in how well suited the databases will be for signal detection of these outcomes with different methods – Examining the variability - an important study component 7
Epidemiologic Design and Statistical Methods Implementation for Primary Analysis PRR USCCS HDPS All ¡other ¡drugs ¡ N/A ¡ Comparator ¡for ¡Humira: ¡Other ¡ Choice of biologics, ¡Comparator ¡for ¡ comparator Pris<q ¡: ¡ ¡An<depressant, ¡ specifically ¡ ¡Cymbalta ¡ ¡(THIN): ¡ used ¡Lexapro ¡as ¡study ¡drug ¡ 1st ¡occurrence ¡of ¡ 1st ¡occurrence ¡ 1st ¡occurrence ¡of ¡the ¡outcome ¡ Case definition the ¡outcome ¡of ¡ of ¡the ¡outcome ¡ of ¡interest ¡ ¡ interest ¡ ¡ of ¡interest ¡ ¡ ¡ Exposure ¡dura<on ¡ Exposure ¡ Ever ¡aLer ¡first ¡prescrip<on ¡of ¡ Risk period plus ¡30 ¡days ¡from ¡ dura<on ¡plus ¡30 ¡ study ¡drug ¡or ¡comparison ¡drug ¡ the ¡end ¡of ¡the ¡ days ¡from ¡the ¡ exposure ¡ ¡ end ¡of ¡the ¡ exposure ¡ ¡ Drug: defined by diagnoses codes NDC codes in Optum and multilexID in THIN. Indication not considered explicitly, but implicit in choice of comparator HDPS: 5 dimensions (prescription, inpatient procedures, outpatient procedure, inpatient diagnoses, outpatient diagnoses) for Optum database; 3 dimensions (prescription, procedure and diagnoses) for THIN database. Data until end of 2010 8
Details of Statistical Method Testing for USCCS • USCCS model considers one drug and one AE which is assumed arising from a non-homogeneous Poisson process Event ¡X ¡ ¡ ¡ ¡Drug ¡A ¡ ¡ ¡A ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡X ¡ • It automatically controls for time-fixed covariates • It only includes cases in the analysis which are then used as their own controls. – The USCCS conditional likelihood – Key assumption: conditionally independent events and events conditionally independent of exposure 9
Details of Statistical Method Testing for HDPS PS Estimation Event X Drug A PS Estimation Event X Drug B • Create cohorts of patients who use drug A or drug B • Estimate Propensity Score (PS) for a given exposure for each individual • based on multiple variables that might be potential confounders (e.g. age, gender, other therapy use) • Risk Estimation then adjusted for confounding using Propensity Scores – PS-based adjustment done with different methods including logistic regression or SMR weighted method 10
Results for Method Testing on Humira Data – Several, but Not All, Pairs Highlighted Humira in Optum AMI GI Perforation Herpes Interstitial Lymphoma Pneumonia Zoster Lung Disease PRR 1.01 ¡(0.85) ¡ 1.33 ¡(1.25) ¡ 1.97 ¡(1.80) ¡ 0.59 ¡(0.57) ¡ 0.89 ¡(0.70) ¡ 0.85 ¡(0.79) ¡ SCCS 1.12(0.87) ¡ 0.78(0.71) ¡ 1.41(1.23) ¡ 0.69(0.66) ¡ 1.43(1.02) ¡ 1.15(1.04) ¡ HDPS 1.22(1.14) ¡ 1.12(1.02) ¡ 0.99(0.95) ¡ 0.82(0.66) ¡ 1.00(0.94) ¡ 0.97(0.83) ¡ Note: Results shown are point estimate with lower bound of 95% confidence interval ( LBCI ) in the parentheses. Highlighted in red are those with LBCI greater than one. 11
Results for Method Testing on Antidepressant Data – Few Pairs Highlighted Pristiq in Optum Lexapro in THIN Orthostatic Orthostatic Fracture Hyperlipidemia Hypertension Proteinuria Fracture Hyperlipidemia Proteinuria Hypertension Hypotension Hypotension PRR 0.50(0.47) 0.37(0.36) 0.37(0.35) 0.81(0.72) 0.46(0.40) 0.21(0.20) 0.31(0.30) 0.19(0.18) 0.49(0.42) 0.30(0.25) USCCS 0.40(0.39) 0.40(0.37) 0.97(0.83) 0.98(0.80) 1.03(0.94) 0.60(0.56) 1.51( 1.22 ) 1.32( 1.06 ) 0.96(0.89) 1.22( 1.14 ) HDPS 0.70(0.67) 0.66(0.62) 0.62(0.61) 0.59(0.53) 0.63(0.54) 1.47( 1.35 ) 1.87( 1.67 ) 1.47( 1.33 ) 1.37( 1.11 ) 1.53( 1.24 ) Note: Point estimates shown with lower bound of 95% confidence interval ( LBCI ) in parentheses LBCI scores greater than 1 are highlighted in red 12
Secondary Analyses • Additional analysis performed for SCCS and HDPS – Varied risk period selections – Varied criteria of case selections (or inclusion criteria) – Examined variation across the two heterogeneous database by also conducting analysis of Lexapro in Optum data for direct comparison to Lexapro versus Cymbalta in THIN • For HDPS – Compared Propensity Score contribution by changing covariates in model, risk estimation method and also reviewed crude estimates – In depth consideration of approaches to PS matching and underlying PS distribution assessment (HDPS) • For SCCS – Examined capability of monitoring change in estimates over time 13
Outcome Criterion change for SCCS Increases Estimates Consistently Pristiq- Humira- Optum Optum • All outcomes highlighted for Humira with incident case • Same trend across study pairs - fractures, hypotension and proteinuria highlighted, Lexapro hypertension and hyperlipideamia less so • Very similar pattern for Lexapro in Optum -THIN data (results not shown) • By contrast varying time at risk period had limited impact ‘New case’ is the subset of ‘all cases’ with at least 12 months enrollment on or before the 1 st outcome 14
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