Pharmacovigilance & Signal Detection 30 th International Conference on Pharmacoepidemiology & Therapeutic Risk Management Pre-conference educational session Thursday, October 23, 2014; 2:00-6:00pm
Semi-automated, distributed, prospective medical product safety monitoring Joshua Gagne, PharmD, ScD, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women s Hospital and Harvard Medical School, USA
Disclosure I am an investigator in the FDA-funded Mini-Sentinel Pilot Project; however opinions expressed here are my own and not necessarily of Mini-Sentinel or FDA I will use Mini-Sentinel to motivate and frame this presentation, but the work presented here was not funded by Mini-Sentinel or FDA
Case study Dabigatran (approved by FDA in October 2010) is an anticoagulant used to reduce the risk of stroke in patients with non-valvular atrial fibrillation It is well known that anticoagulation therapy can cause serious (including fatal) bleeding RCT of 18,000 patients found similar major bleed rates between patients assigned to dabigatran versus warfarin By December 2011, FDA had received many more reports of serious bleeding events than expected If you were FDA, what would you do?
Drug safety information sources Spontaneous adverse event reports Metaanalyses of completed trials Observational studies Ongoing trials Adapted from Eichler HG et al. Clin Pharmacol Ther 2012;91:426-37
Drug safety information sources Spontaneous adverse event reports Metaanalyses of completed trials Observational studies Ongoing trials Active monitoring Adapted from Eichler HG et al. Clin Pharmacol Ther 2012;91:426-37
Observational studies of rofecoxib and MI Source: McGettigan et al. JAMA 2006;296:1633-44.
Types of surveillance activities Four types of medical product safety surveillance activities in electronic healthcare data: Temporal perspective Retrospective Prospective Outcome specification Prespecified Non-prespecified Ordinary pharmacoepi studies Data mining Prospective pharmacoepi analyses Syndromic surveillance
Electronic healthcare data Claims data Member ID Plan Gender Age Dates of Eligibility Member ID Prescribing physician Drug dispensed (NDC) Quantity and date dispensed Drug strength Days supply Dollar amounts Member ID Physician or Facility identifier Procedures (CPT-4, revenue codes, ICD-9) Diagnosis (ICD-9-CM, DRG) Admission and discharge dates Date and place of service Dollar amounts Supplemental data Member ID Lab Test Name Result Member ID Income Net Worth Education Race & Ethnicity Life Stage Life Style Indicators Member ID Subspecialty notes Endoscopy reports Histology reports Radiology reports Free text notes Administrative Data Pharmacy Claims Data Physician and Facility Claims Data Lab Test Results Data Consumer Elements Electronic Medical Records Computerized linked longitudinal dataset 9 Courtesy of Sebastian Schneeweiss, MD, ScD
FDA s Sentinel Initiative a brief history 2007: FDAAA mandates FDA to establish active surveillance system for monitoring drugs using electronic healthcare data 2008: FDA establishes Sentinel Initiative which aims to develop and implement proactive system that will complement existing systems to track adverse events linked to its regulated products 2009-2014: FDA sponsors Mini-Sentinel pilot project to develop scientific operations for active medical product safety surveillance
Mini-Sentinel s Distributed Database Source: Platt R. FDA s Mini-Sentinel program to evaluate the safety of marketed medical products; 1/18/2012.
Opportunities for researchers Generate safety information as quickly as possible, by analyzing data: Across multiple data sources while maintaining data confidentiality and privacy Prospectively while accounting for accumulating data Semi-automatically while maintaining flexibility in design and analysis strategies Active safety monitoring requires an approach that is distributed, prospective, and semi-automated while maintaining validity of effect estimation
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Incorporating data as they accrue
Challenges to prospective monitoring All of the challenges of typical studies: Confounding Selection bias Misclassification Etc, etc Trade-offs between: Sharing necessary data while maintaining data privacy in distributed data environment Analyzing data as quickly as possible while addressing issues related to small numbers samples and few outcomes Semi-automating analytic processes while preserving broad functionality
Matched cohort analyses are one solution Propensity scores: Reflect patients probability of receiving a treatment (e.g., drug X) conditional on their measured confounders Summarize all confounders in a single score Anonymize patient-level data (facilitate sharing patient-level information) Facilitate adjustment for many, many confounders even for rare events Permit simultaneous confounding control for multiple outcomes Facilitates application of maximized sequential probability ratio test C 1 æ Pr(X =1) ö lnç = b 0 + b 1 C 1 + b 2 C 2 + b 3 C 3 è1- Pr(X =1) ø C 2 C 3 X Y
Sequential propensity scores PS-match PS-match PS-match
Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1... DP n D _ D E _ E 10 5 90 95 100 100 15 10 185 190 200 200 25 15 275 285 300 300
Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1. D _ D E _ E 10 5 90 95 100 100 15 10 185 190 200 200 25 20 275 280 300 300 50 40 450 460 500 500.. 15 10 30 20 40 30 100 75 DP n 185 190 370 380 560 570 900 925 200 200 400 400 600 600 1000 1000 25 15 45 30 65 50 150 115 275 285 555 570 835 850 1350 1385 300 300 600 600 900 900 1500 1500
Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1. D _ D E _ E 10 5 90 95 100 100 15 10 185 190 200 200 25 20 275 280 300 300 50 40 450 460 500 500 100 75 1000 1025 1100 1100.. 15 10 30 20 40 30 100 75 185 135 DP n 185 190 370 380 560 570 900 925 2015 2065 200 200 400 400 600 600 1000 1000 2200 2200 25 15 45 30 65 50 150 115 285 210 275 285 555 570 835 850 1350 1385 3015 3090 300 300 600 600 900 900 1500 1500 3300 3300
Semi-automating the design Fixed covariate assessment period Follow-up period x Time Initiation of exposure Start of follow-up Event of interest Inputs: Drug(s) and comparator(s) of interest with exposure risk window Outcome of interest and duration of follow-up Pre-defined covariates and duration of baseline assessment period High-dimensional propensity score parameters/options
Semi-automated workflow Coordinating center Define parameters Multiple data partners Create code and files Select alerting algorithm Evaluate diagnostics and determine how to proceed Transmit code and files to data partners Transmit data to OC Identify cohort, outcomes, covariates and fit confounder propensity score Aggregate data across partners Apply alerting algorithm 10.0 Disseminate results for decision support 8.0 6.0 4.0 2.0 0.0-2.0-4.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Reiterate at next data update -6.0-8.0-10.0
When to generate an alert? PS-match PS-match PS-match
When to generate an alert? PS-match PS-match PS-match
When to generate an alert? PS-match PS-match PS-match
Rofecoxib vs. ns-nsaids and MI
Cerivastatin vs. atorvastatin and rhabdo Source: Gagne JJ et al. Epidemiology 2012;23:238-46.
Celecoxib vs. ns-nsaids and MI 20.0 15.0 10.0 5.0 0.0-5.0-10.0-15.0-20.0 1 2 Lower 95% confidence interval -20.60-9.43 Cumulative rate difference -2.28 2.96 Upper 95% confidence interval 16.04 15.35 Cumulative events: celecoxib 4 23 Cumulative events: ns-nsaids 4 12 Cumulative person-years: celecoxib 249 635 Cumulative person-years: ns-nsaids 176 374 3 4 5-9.35-11.63-9.98 0.38-2.49-1.43 10.12 6.65 7.12 33 42 51 18 26 29 1028 1425 1791 571 777 965 6-7.92 0.00 7.93 58 30 2136 1142 7-8.29-0.73 6.83 62 33 3717 2317 8-6.15 1.05 8.25 71 34 5029 3237 9-4.68 0.77 6.21 94 52 5728 3676 10-4.73 0.08 4.90 120 71 6295 4056 11-3.05 1.53 6.10 143 77 6807 4357 12-3.30 1.09 5.48 154 85 7239 4615 13-2.98 1.31 5.60 170 92 7613 4834 14-2.23 1.93 6.08 185 96 7971 5037 15-2.47 1.53 5.53 188 99 8277 5212 16-2.37 1.58 5.52 201 105 8619 5411 17-2.73 1.14 5.02 206 110 8960 5617 18-2.51 1.28 5.07 214 113 9276 5804 19-1.93 1.77 5.47 223 114 9556 5963 20-2.33 1.33 4.98 229 120 9821 6123
Rosuvastatin vs. atorvastatin and rhabdo Source: Gagne JJ et al. Clinical Pharmacol Ther 2012;92:80-6.
Summary Many challenges to generating valid drug safety information in post-marketing setting Active monitoring can provide: Utilization patterns with known denominators Who receives product Concomitant drug use And how these change over time (REMS; impact of FDA actions) Rates of adverse events following use of medical products (and corresponding rates for many other populations) Ability to conduct semi-automated, distributed, and prospective medical product safety analyses
Drug safety information generation Spontaneous adverse event reports Active monitoring Observational Metastudies analyses of Ongoing trials completed trials Adapted from Eichler HG et al. Clin Pharmacol Ther 2012;91:426-37