Pharmacovigilance & Signal Detection

Transcription

1 Pharmacovigilance & Signal Detection 30 th International Conference on Pharmacoepidemiology & Therapeutic Risk Management Pre-conference educational session Thursday, October 23, 2014; 2:00-6:00pm

2 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

3 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

4 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?

5 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

6 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

7 Observational studies of rofecoxib and MI Source: McGettigan et al. JAMA 2006;296:

8 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

9 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

10 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 : FDA sponsors Mini-Sentinel pilot project to develop scientific operations for active medical product safety surveillance

11 Mini-Sentinel s Distributed Database Source: Platt R. FDA s Mini-Sentinel program to evaluate the safety of marketed medical products; 1/18/2012.

12 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

13 Incorporating data as they accrue

14 Incorporating data as they accrue

15 Incorporating data as they accrue

16 Incorporating data as they accrue

17 Incorporating data as they accrue

18 Incorporating data as they accrue

19 Incorporating data as they accrue

20 Incorporating data as they accrue

21 Incorporating data as they accrue

22 Incorporating data as they accrue

23 Incorporating data as they accrue

24 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

25 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

26 Sequential propensity scores PS-match PS-match PS-match

27 Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1... DP n D _ D E _ E

28 Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1. D _ D E _ E DP n

29 Data aggregation Launch date + 3 mos. + 3 mos. + 3 mos. + 3 mos. + 3 mos. DP 1. D _ D E _ E DP n

30 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

31 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 Reiterate at next data update

32 When to generate an alert? PS-match PS-match PS-match

33 When to generate an alert? PS-match PS-match PS-match

34 When to generate an alert? PS-match PS-match PS-match

35 Rofecoxib vs. ns-nsaids and MI

36 Cerivastatin vs. atorvastatin and rhabdo Source: Gagne JJ et al. Epidemiology 2012;23:

37 Celecoxib vs. ns-nsaids and MI Lower 95% confidence interval Cumulative rate difference Upper 95% confidence interval Cumulative events: celecoxib 4 23 Cumulative events: ns-nsaids 4 12 Cumulative person-years: celecoxib Cumulative person-years: ns-nsaids

38 Rosuvastatin vs. atorvastatin and rhabdo Source: Gagne JJ et al. Clinical Pharmacol Ther 2012;92:80-6.

39 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

40 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