Evaluation of the US Food and Drug Administration Sentinel Analysis Tools Using a Comparator with a Different Indication: Comparing the Rates of Gastrointestinal Bleeding in Warfarin and Statin Users.


Journal

Pharmaceutical medicine
ISSN: 1179-1993
Titre abrégé: Pharmaceut Med
Pays: New Zealand
ID NLM: 101471195

Informations de publication

Date de publication:
02 2019
Historique:
entrez: 15 1 2020
pubmed: 15 1 2020
medline: 29 4 2020
Statut: ppublish

Résumé

The US Food and Drug Administration's Sentinel System was established to monitor safety of regulated medical products. Sentinel investigators identified known associations between drugs and adverse events to test reusable analytic tools developed for Sentinel. This test case used a comparator with a different indication. We tested the ability of Sentinel's reusable analytic tools to identify the known association between warfarin and gastrointestinal bleeding (GIB). Statins, expected to have no effect on GIB, were the comparator. We further explored the impact of analytic features, including matching ratio and stratifying Cox regression analyses, on matched pairs. This evaluation included data from 14 Sentinel Data Partners. New users of warfarin and statins, aged 18 years and older, who had not received other anticoagulants or had recent GIB were matched on propensity score using 1:1 and 1:n variable ratio matching, matching statin users with warfarin users to estimate the average treatment effect in warfarin-treated patients. We compared the risk of GIB using Cox proportional hazards regression, following patients for the duration of their observed continuous treatment or until a GIB. For the 1:1 matched cohort, we conducted analyses with and without stratification on matched pair. The variable ratio matched cohort analysis was stratified on the matched set. We identified 141,398 new users of warfarin and 2,275,694 new users of statins. In analyses stratified on matched pair/set, the hazard ratios (HR) for GIB in warfarin users compared with statin users were 2.78 (95% confidence interval [CI] 2.36-3.28) in the 1:1 matched cohort and 3.10 (95% CI 2.76-3.49) in the variable ratio matched cohort. The HR was lower in the analysis of the 1:1 matched cohort not stratified by matched pair (2.22, 95% CI 1.97-2.49), and highest early in treatment. Follow-up for warfarin users tended to be shorter than for statin users. This study identified the expected GIB risk with warfarin compared with statins using an analytic tool developed for Sentinel. Our findings suggest that comparators with different indications may be useful in surveillance in select circumstances. Finally, in the presence of differential censoring, stratification by matched pair may reduce the potential for bias in Cox regression analyses.

Sections du résumé

BACKGROUND
The US Food and Drug Administration's Sentinel System was established to monitor safety of regulated medical products. Sentinel investigators identified known associations between drugs and adverse events to test reusable analytic tools developed for Sentinel. This test case used a comparator with a different indication.
OBJECTIVE
We tested the ability of Sentinel's reusable analytic tools to identify the known association between warfarin and gastrointestinal bleeding (GIB). Statins, expected to have no effect on GIB, were the comparator. We further explored the impact of analytic features, including matching ratio and stratifying Cox regression analyses, on matched pairs.
METHODS
This evaluation included data from 14 Sentinel Data Partners. New users of warfarin and statins, aged 18 years and older, who had not received other anticoagulants or had recent GIB were matched on propensity score using 1:1 and 1:n variable ratio matching, matching statin users with warfarin users to estimate the average treatment effect in warfarin-treated patients. We compared the risk of GIB using Cox proportional hazards regression, following patients for the duration of their observed continuous treatment or until a GIB. For the 1:1 matched cohort, we conducted analyses with and without stratification on matched pair. The variable ratio matched cohort analysis was stratified on the matched set.
RESULTS
We identified 141,398 new users of warfarin and 2,275,694 new users of statins. In analyses stratified on matched pair/set, the hazard ratios (HR) for GIB in warfarin users compared with statin users were 2.78 (95% confidence interval [CI] 2.36-3.28) in the 1:1 matched cohort and 3.10 (95% CI 2.76-3.49) in the variable ratio matched cohort. The HR was lower in the analysis of the 1:1 matched cohort not stratified by matched pair (2.22, 95% CI 1.97-2.49), and highest early in treatment. Follow-up for warfarin users tended to be shorter than for statin users.
CONCLUSIONS
This study identified the expected GIB risk with warfarin compared with statins using an analytic tool developed for Sentinel. Our findings suggest that comparators with different indications may be useful in surveillance in select circumstances. Finally, in the presence of differential censoring, stratification by matched pair may reduce the potential for bias in Cox regression analyses.

Identifiants

pubmed: 31933271
doi: 10.1007/s40290-018-00265-w
pii: 10.1007/s40290-018-00265-w
doi:

Substances chimiques

Anticoagulants 0
Hydroxymethylglutaryl-CoA Reductase Inhibitors 0
Warfarin 5Q7ZVV76EI

Types de publication

Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

29-43

Subventions

Organisme : FDA HHS
ID : HHSF223200910006I
Pays : United States
Organisme : FDA HHS
ID : HHSF22301008T
Pays : United States
Organisme : FDA HHS
ID : HHSF223201400030I
Pays : United States

Références

PLoS One. 2011;6(6):e21447
pubmed: 21731754
Am J Epidemiol. 2001 Nov 1;154(9):854-64
pubmed: 11682368
J Clin Epidemiol. 2001 Apr;54(4):387-98
pubmed: 11297888
Circulation. 2007 Jan 2;115(1):27-33
pubmed: 17179016
Gastrointest Endosc Clin N Am. 2015 Jul;25(3):415-28
pubmed: 26142028
EGEMS (Wash DC). 2016 Oct 14;4(1):1234
pubmed: 27891526
J Bone Miner Res. 2011 Apr;26(4):683-8
pubmed: 20939064
Biol Blood Marrow Transplant. 2016 Mar;22(3):557-63
pubmed: 26712591
Pharmacoepidemiol Drug Saf. 2011 Jun;20(6):560-6
pubmed: 21387461
Thromb Haemost. 2001 Aug;86(2):563-8
pubmed: 11522004
Arch Intern Med. 2001 Sep 24;161(17):2125-8
pubmed: 11570942
Am J Epidemiol. 2011 Dec 1;174(11):1213-22
pubmed: 22025356
Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:18-22
pubmed: 22262589
J Clin Epidemiol. 2011 Jul;64(7):749-59
pubmed: 21208778
Pharmacoepidemiol Drug Saf. 2011 Jun;20(6):551-9
pubmed: 21394812
Int J Cardiol. 2017 Feb 1;228:761-770
pubmed: 27888753
Chest. 2008 Jun;133(6 Suppl):257S-298S
pubmed: 18574268
Clin Ther. 2013 Jul;35(7):967-984.e2
pubmed: 23870607
Pharmacoepidemiol Drug Saf. 2012 Jul;21(7):697-709
pubmed: 22162077
Br J Clin Pharmacol. 2009 Apr;67(4):460-5
pubmed: 19371320
Pharmacoepidemiol Drug Saf. 2012 Jan;21 Suppl 1:12-7
pubmed: 22262588
Multivariate Behav Res. 2011 May;46(3):399-424
pubmed: 21818162
J Gen Intern Med. 1998 May;13(5):311-6
pubmed: 9613886
Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):890-899
pubmed: 28397352
Am J Epidemiol. 2016 May 15;183(10):930-6
pubmed: 27189329
Stat Med. 2016 Jan 30;35(2):294-304
pubmed: 26278725
Pharmacoepidemiol Drug Saf. 2018 Jul;27(7):731-739
pubmed: 29532543
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:69-80
pubmed: 22552982
J Gen Intern Med. 2011 May;26(5):546-50
pubmed: 21203857
Best Pract Res Clin Gastroenterol. 2008;22(2):225-32
pubmed: 18346680
Curr Epidemiol Rep. 2015 Dec;2(4):221-228
pubmed: 26954351
Ann Intern Med. 2013 Dec 3;159(11):793-4
pubmed: 24297203
Epidemiol Rev. 2003;25:43-50
pubmed: 12923989
Am J Med. 2010 Feb;123(2):151-7
pubmed: 20103024
Clin Pharmacol Ther. 2016 Nov;100(5):558-564
pubmed: 27416001
Circulation. 2011 Nov 15;124(20):2233-42
pubmed: 22007076
N Engl J Med. 2009 Aug 13;361(7):645-7
pubmed: 19635947
Stat Med. 2013 Jul 20;32(16):2837-49
pubmed: 23239115
Pharmacoepidemiol Drug Saf. 2015 Aug;24(8):849-57
pubmed: 26095209
Epidemiology. 2017 Nov;28(6):838-846
pubmed: 28682851
N Engl J Med. 2018 Nov 29;379(22):2091-2093
pubmed: 30485777

Auteurs

Ryan M Carnahan (RM)

Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Dr., S437 CPHB, Iowa City, IA, 52242, USA. Ryan-Carnahan@uiowa.edu.

Joshua J Gagne (JJ)

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Christian Hampp (C)

Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.

Charles E Leonard (CE)

Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Sengwee Toh (S)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Candace C Fuller (CC)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Sean Hennessy (S)

Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Laura Hou (L)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Noelle M Cocoros (NM)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Genna Panucci (G)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Tiffany Woodworth (T)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Austin Cosgrove (A)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Aarthi Iyer (A)

Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.

Elizabeth A Chrischilles (EA)

Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Dr., S437 CPHB, Iowa City, IA, 52242, USA.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH