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).


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



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Characteristics of Multi-database studies


‘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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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

Raw dataRaw dataRaw dataRaw dataRaw data




Strategy A

Strategy B

Strategy C

Strategy D

GeneralCDM

GeneralCDM

GeneralCDM

GeneralCDM

GeneralCDM

Study-specific

extraction

Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

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Study-specific

extraction

Studyvariables

Studyvariables

Studyvariables

Studyvariables

Studyvariables

AnalyticdatasetAnalyticdatasetAnalyticdatasetAnalyticdatasetAnalyticdataset

Studyestimates

Studyestimates

Studyestimates

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Studyestimates

Pooledstudy

estimates

Pooledextractedraw data

Studyvariables

Pooledanalyticdataset

Pooledstudy

estimates

Study-specificCDM

Study-specificCDM

Study-specificCDM

Study-specificCDM

Study-specificCDM

Studyvariables

Studyvariables

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CC

Studyvariables

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CC

CC

Studyestimates

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CC

CC

Pooledstudy

estimates

Pooledstudy

estimates

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


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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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


Dabigatran

Rivaroxaban

Apixaban

Individual DOACs and risk of major bleeding

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Individual DOACs and risk of intracranial bleed

Dabigatran

Rivaroxaban

Apixaban


DOACs and risk of major bleed by age

Age <75

Age >=75

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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


Nordic: ssri during pregnancy and cv birth defect

Selmer R, et al. Pharmacoepidemiol Drug Saf 2016; 25:1160-9

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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


EU-ADR: NSAIDs and Upper Gastrointestinal Bleeding

Coloma P, et al. Pharmacoepidemiol Drug Saf 2011;20:1-11

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FDA Sentinel: ACE inhibitors and angioedema

Toh S, et al. Pharmacoepidemiol Drug Saf 2013;22:1171-7


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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For 7 out of 53 D-O pairs design/analysis choices deemed appropriate

17% of negative controls potentially misclassified


Suchard MA, et al. Lancet 2019;Oct 24 2019, published online

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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?


Missing data in Multi DatabaseStudies

Completely missing

Partially missing

PROTECT: Antidepressants andrisk of hip fracture

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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.


Subj

ects

Variables

Romin Pajouheshnia, presented at ICPE 2019 Philadelphia

Missing data in MDBS

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Missing data in MDBS

?Subjects

Variables


Omit people…or databases?

?Subjects

Variables

• Loss of precision- And clinical (“good”) heterogeneity

• Selection bias?

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Omit variables?

?Subjects

Variables

• Residual confounding?

• Study feasibility?


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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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)


Future prospects: MLMI across DDN

Area for development

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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

Design and analysis of multi- country, multi-database … · 2019. 11. 4. · 11/2/19 1...

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