design and analysis of multi- country, multi-database … · 2019. 11. 4. · 11/2/19 1...
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Farmaco-epidemiologie & Klinische Farmacologie
Design and analysis of multi-country, multi-database pharmacoepidemiologic
studies
Olaf H Klungel, PharmD, PhD, FISPE
Utrecht Institute for Pharmaceutical Sciences (UIPS)Division of Pharmacoepidemiology & Clinical Pharmacology
Research Unit of Clinical Pharmacology and Pharmacy, Faculty of Health Sciences, University of Southern Denmark (SDU).
Farmaco-epidemiologie & Klinische Farmacologie
Rationale for Multi Database studies
• Increased sample size– Precise estimates for rare exposures/outcomes– Subgroup effects– Short time window (early months following
approval)
• Reproducibility– Consistent results when utilizing same
methodology in heterogeneous populations
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Bazelier MT, et al. Pharmacoepidemiol Drug Saf 2015;24:897-905
8 studies
14 studies
Farmaco-epidemiologie & Klinische Farmacologie
Bazelier MT, et al. Pharmacoepidemiol Drug Saf 2015;24:897-905
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Farmaco-epidemiologie & Klinische Farmacologie
Bazelier MT, et al. Pharmacoepidemiol Drug Saf 2015;24:897-905
Characteristics of Multi-database studies
Farmaco-epidemiologie & Klinische Farmacologie
‘Increasing harmonization’: the evolution
across FP-7 & IMI EU-funded drug safety projects
Common protocol, no CDM
• PROTECT, GetReal, EU PE&PV safety of DOACs
Project based CDM
• EU-ADR (2008), SOS (2009), ARITMO (2010), VAESCO (2010), SAFEGUARD (2011)…. EMIF (2012), ADVANCE (2013), CARING (2011)
Common scripts: Jerboa, SAS, R
datawarehouse &
poolingADVANCE
Codemapper
OMOP CDMtest
• EU-ADR (2008), SOS (2009), ARITMO (2010), VAESCO (2010), SAFEGUARD (2011)…. EMIF (2012), ADVANCE (2013), CARING (2011)
• SOS project (2009) Datawarehouse at Milano-Bicocca CARING (2011) pooling and centralised analysis at Statistics Denmark
• ADVANCE project (2013)Conception (2018)EU PE&PV valproate/retinoids effectiveness of RMM
• EMIF project (2017), Bigdata@heart (2017)EHDN project (2018)
Courtesy: [email protected]
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Farmaco-epidemiologie & Klinische Farmacologie
Strategies to execute multi-database studies
Gini R et al, on behalf of the Working Group 3 of ENCePP. Different strategies to executemulti-database studies for drug surveillance in real world setting: a reflection on the European model.Manuscript in preparation
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Strategy A
Strategy B
Strategy C
Strategy D
GeneralCDM
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Study-specificCDM
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Protocoldevelopment
andagreement
Protocolapproval
Data transformation Data analysis
Distributed dataExecution of localprocedures
CC
Local execution of aprocedure shared by acoordinating centre
Sharing data with acoordinating centre Centralised data
Central executionof a procedure
Farmaco-epidemiologie & Klinische Farmacologie
Strategy A: common protocol/no CDMPROTECT/EU PE&PV, CNODES
– No sharing of individual patient data– Overall results are collected for meta-analysis– Allows optimization for individual database– Common protocol with sufficient detail implemented at single site– Control for confounding
» Conventional Multivariable Regression» Common set of confounders» Additional adjustment in individual databases with maximum
amount of information» hdPS
– Blinding of site-specific results
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Farmaco-epidemiologie & Klinische Farmacologie
DOACs and risk of major bleeding
EU PE&PV Network; EMA Framework service contract (nr. EMA/2015/27/PH)Van den Ham, Souverein PC, Klungel OH, et al. Risk of Major Bleeding associated with the use of individualdirect oral anticoagulants compared to vitamin K antagonists in patients with non-valvular atrial fibrillation: a meta-analysis of results from multiple population-based cohort studies using a common protocol in Europe and Canada. Submitted.
CN
OD
ESEU
PE&
PV
Farmaco-epidemiologie & Klinische Farmacologie
Dabigatran
Rivaroxaban
Apixaban
Individual DOACs and risk of major bleeding
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Farmaco-epidemiologie & Klinische Farmacologie
Individual DOACs and risk of intracranial bleed
Dabigatran
Rivaroxaban
Apixaban
Farmaco-epidemiologie & Klinische Farmacologie
DOACs and risk of major bleed by age
Age <75
Age >=75
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Farmaco-epidemiologie & Klinische Farmacologie
Strategy B: Sharing raw individual level data
– Individual patient data collected from all databases for one common analysis
– Common analysis on individual patient dataset» Control for confounding limited by number of
confounders that are common to each database
» Can be complemented by meta-analysis utilizing site-optimized estimates
Farmaco-epidemiologie & Klinische Farmacologie
Nordic: ssri during pregnancy and cv birth defect
Selmer R, et al. Pharmacoepidemiol Drug Saf 2016; 25:1160-9
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Farmaco-epidemiologie & Klinische Farmacologie
Strategy C: Study Specific CDM
EU-ADR, EU PE&PV, Sentinel
– Stratified datasets collected from all databases– Outcomes, Exposure, Covariate patterns– One common analysis
» Control for confounding limited by number of confounders stratified on
» Case-centered logistic regression
Farmaco-epidemiologie & Klinische Farmacologie
EU-ADR: NSAIDs and Upper Gastrointestinal Bleeding
Coloma P, et al. Pharmacoepidemiol Drug Saf 2011;20:1-11
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Farmaco-epidemiologie & Klinische Farmacologie
FDA Sentinel: ACE inhibitors and angioedema
Toh S, et al. Pharmacoepidemiol Drug Saf 2013;22:1171-7
Farmaco-epidemiologie & Klinische Farmacologie
Strategy D: general CDM
- General Common Data Model- All design/analysis options applied to all 53 D-O pairs in multiple DBs
Ryan P, et al. Drug Saf 2013;36 (Suppl 1):S143-58
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Farmaco-epidemiologie & Klinische Farmacologie
For 7 out of 53 D-O pairs design/analysis choices deemed appropriate
17% of negative controls potentially misclassified
Farmaco-epidemiologie & Klinische Farmacologie
Suchard MA, et al. Lancet 2019;Oct 24 2019, published online
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Farmaco-epidemiologie & Klinische Farmacologie
Basic Pharmacoepi principles?
• For outcome of myocardial infarction only subjects withhistory of MI excluded– Angina pectoris=> CCB => confounding by indication!
• Control for confounding by PS matching– Data-driven selection of co-variates, not necessarily risk
factors for MI => over/under- adjustment
• Negative controls => true negative?
Farmaco-epidemiologie & Klinische Farmacologie
Missing data in Multi DatabaseStudies
Completely missing
Partially missing
PROTECT: Antidepressants andrisk of hip fracture
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Farmaco-epidemiologie & Klinische Farmacologie
PROTECT: Antidepressants and risk of Hip Fracture
Souverein P, et al. PDS 2016;25 (Suppl 1):88-102.De Groot M, et al. PDS 2016;25 (Suppl 1):103-13.
Farmaco-epidemiologie & Klinische Farmacologie
Subj
ects
Variables
Romin Pajouheshnia, presented at ICPE 2019 Philadelphia
Missing data in MDBS
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Farmaco-epidemiologie & Klinische Farmacologie
Missing data in MDBS
?Subjects
Variables
Farmaco-epidemiologie & Klinische Farmacologie
Omit people…or databases?
?Subjects
Variables
• Loss of precision- And clinical (“good”) heterogeneity
• Selection bias?
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Farmaco-epidemiologie & Klinische Farmacologie
Omit variables?
?Subjects
Variables
• Residual confounding?
• Study feasibility?
Farmaco-epidemiologie & Klinische Farmacologie
In practice
In multi-database context
à Multiple imputation within each cohort
• Are we making the most of our data?
• What about completely missing variables?
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Farmaco-epidemiologie & Klinische Farmacologie
Leveraging information across databases
Multi-level multiple imputation
- Borrow information from one or more databases to make predictions (imputations) in the database completely missing the variable(s)
Farmaco-epidemiologie & Klinische Farmacologie
Future prospects: MLMI across DDN
Area for development
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Farmaco-epidemiologie & Klinische Farmacologie
Farmaco-epidemiologie & Klinische Farmacologie
Conclusions and recommendations
• Tailor design and analysis to specific drug-event association
• Harmonization across DBs at different levels– Common protocol: Design, analysis, exposure, outcome,
confounder– Common Data Model: Exposure, outcome, confounder
• General CDM• Study specific CDM
• When power is no issue, even more critical to address bias
• Pre-defined sensitivity analysis on methodological parameters