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Invited Commentary
Diabetes and Endocrinology
December 21, 2018

A Second Opinion From Observational Data on Second-line Diabetes Drugs

Author Affiliations
  • 1Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California
JAMA Netw Open. 2018;1(8):e186119. doi:10.1001/jamanetworkopen.2018.6119

Recommendations vary for second-line treatment of type 2 diabetes, with little consensus even among clinical guidelines1,2 in which the primary end point is maintenance of a blood hemoglobin A1c level less than 7%. Multiple prior studies also have contradictory results in terms of risk of adverse outcomes, including cardiovascular events and kidney disease.3,4

The study by O’Brien et al5 makes an important contribution to this area, by assessing the effectiveness of second-line treatment options for type 2 diabetes in reducing the risk of cardiovascular events (stroke, congestive heart failure, ischemic heart disease, and peripheral artery disease). Analyzing a large commercial claims data set, they found that risk of cardiovascular events did not differ between either sodium-glucose cotransporter 2 inhibitors or thiazolidinediones and dipeptidyl peptidase 4 inhibitors, and that risk was increased for sulfonylureas or meglitinides and basal insulin in comparison with dipeptidyl peptidase 4 inhibitors. Their findings agree with our recent study6 examining the association between classes of second-line type 2 diabetes therapies and blood glucose control as well as risk of adverse events (myocardial infarction, kidney disorders, and eye disorders). O’Brien et al5 make a valuable contribution by examining an additional drug class, glucagon-like peptide 1 receptor agonists, and finding that, in some analyses, risk of cardiovascular events was lower for glucagon-like peptide 1 receptor agonists in comparison with dipeptidyl peptidase 4 inhibitors.

Both the study by O’Brien et al5 and our previous study leverage observational data that capture details of health care processes and patient outcomes for millions of lives, with significant longitudinal coverage. Such data are increasingly accessible via commercial claims databases available for purchase and data provider networks such as the Observational Health Data Sciences and Informatics initiative,7 which also provides a common data model and an associated suite of open source analytic tools. Observational studies carried out at the scale enabled by such resources make it possible to investigate the efficacy of an ever-growing pool of treatments and their associated risks of adverse outcomes. The increasing ease of conducting such studies with these large data sources makes it essential both to conduct rigorous analyses and to make the details of these analyses transparent.

The study by O’Brien et al5 and the experience in the Observational Health Data Sciences and Informatics network has surfaced the following essential properties of rigorous and meaningful observational studies: (1) clearly stating the type of question and study; (2) exploratory analyses of the data source to ensure that the data are appropriate to answer the question of interest in terms of existence of necessary variables (eg, hemoglobin A1c blood test results) and years covered, especially when studying drugs that enter the market in specific years; (3) reporting precise and reproducible cohort definitions; (4) conducting sensitivity analyses and reporting their results in the main conclusions; (5) empirically calibrating significance thresholds8 and confidence intervals9; and (6) emphasizing study replication across sites and data sources.10

Study replication is especially important because if multiple researchers ask the same clinical question over time, across data sources and settings, and with differing study designs, the conclusions drawn from them are collectively stronger. The study by O’Brien et al5 targets an area of significant clinical uncertainty with the potential to inform the treatment of millions of individuals with type 2 diabetes. The study states the question type, clearly reports cohort definitions, and conducts sensitivity analyses (eg, note that after performing sensitivity analyses, they found that the seemingly lower risk of composite cardiovascular events with glucagon-like peptide 1 receptor agonists was no longer significant).

To advance a learning health system as a community, clinical researchers need to take advantage of observational data sources. To generate findings that can be trusted, clinical researchers need to adopt good practices in the analyses of observational data. JAMA Network Open, with its emphasis on ensuring rigorous, transparent, and reproducible studies, is leading the way in making the design and conduct of such research a norm.

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Article Information

Published: December 21, 2018. doi:10.1001/jamanetworkopen.2018.6119

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Callahan A et al. JAMA Network Open.

Corresponding Author: Nigam H. Shah, MBBS, PhD, Center for Biomedical Informatics Research, Stanford University School of Medicine, 1265 Welch Rd, Room X-235, Stanford, CA 94025 (nigam@stanford.edu).

Conflict of Interest Disclosures: Dr Shah reported receiving grants from the National Institutes of Health during the writing of this article. No other disclosures were reported.

References
1.
Marathe  PH, Gao  HX, Close  KL.  American Diabetes Association standards of medical care in diabetes 2017.  J Diabetes. 2017;9(4):320-324. doi:10.1111/1753-0407.12524PubMedGoogle ScholarCrossref
2.
Garber  AJ, Abrahamson  MJ, Barzilay  JI,  et al.  Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm—2017 executive summary.  Endocr Pract. 2017;23(2):207-238. doi:10.4158/EP161682.CSPubMedGoogle ScholarCrossref
3.
Bennett  WL, Maruthur  NM, Singh  S,  et al.  Comparative effectiveness and safety of medications for type 2 diabetes: an update including new drugs and 2-drug combinations.  Ann Intern Med. 2011;154(9):602-613. doi:10.7326/0003-4819-154-9-201105030-00336PubMedGoogle ScholarCrossref
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Wilkinson  SV, Tomlinson  LA, Iwagami  M, Stirnadel-Farrant  HA, Smeeth  L, Douglas  I.  A systematic review comparing the evidence for kidney function outcomes between oral antidiabetic drugs for type 2 diabetes.  Wellcome Open Res. 2018;3:74. doi:10.12688/wellcomeopenres.14660.1PubMedGoogle ScholarCrossref
5.
O’Brien  MJ, Karam  SL, Wallia  A,  et al.  Association of second-line antidiabetic medications with cardiovascular events among insured adults with type 2 diabetes.  JAMA Netw Open. 2018;1(8):e186125. doi:10.1001/jamanetworkopen.2018.6125Google Scholar
6.
Vashisht  R, Jung  K, Schuler  A,  et al.  Association of hemoglobin A1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: analysis from the Observational Health Data Sciences and Informatics initiative.  JAMA Netw Open. 2018;1(4):e181755. doi:10.1001/jamanetworkopen.2018.1755Google ScholarCrossref
7.
Hripcsak  G, Duke  JD, Shah  NH,  et al.  Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers.  Stud Health Technol Inform. 2015;216:574-578.PubMedGoogle Scholar
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Schuemie  MJ, Hripcsak  G, Ryan  PB, Madigan  D, Suchard  MA.  Robust empirical calibration of P-values using observational data.  Stat Med. 2016;35(22):3883-3888. doi:10.1002/sim.6977PubMedGoogle ScholarCrossref
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Schuemie  MJ, Hripcsak  G, Ryan  PB, Madigan  D, Suchard  MA.  Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data.  Proc Natl Acad Sci U S A. 2018;115(11):2571-2577. doi:10.1073/pnas.1708282114PubMedGoogle ScholarCrossref
10.
Hripcsak  G, Ryan  PB, Duke  JD,  et al.  Characterizing treatment pathways at scale using the OHDSI network.  Proc Natl Acad Sci U S A. 2016;113(27):7329-7336. doi:10.1073/pnas.1510502113PubMedGoogle ScholarCrossref
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