Claims-based studies of oral glucose-lowering medications can achieve balance in critical clinical variables only observed in electronic health records.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 100883645 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1463-1326 (Electronic) Linking ISSN: 14628902 NLM ISO Abbreviation: Diabetes Obes Metab Subsets: MEDLINE
    • Publication Information:
      Original Publication: Oxford : Wiley-Blackwell, c1999-
    • Subject Terms:
      Blood Glucose/*drug effects ; Diabetes Mellitus, Type 2/*drug therapy ; Diabetes Mellitus, Type 2/*epidemiology ; Electronic Health Records/*statistics & numerical data ; Hypoglycemic Agents/*administration & dosage ; Linagliptin/*administration & dosage; Administration, Oral ; Administrative Claims, Healthcare/statistics & numerical data ; Adult ; Aged ; Blood Glucose/metabolism ; Clinical Trials as Topic/statistics & numerical data ; Cohort Studies ; Databases, Factual ; Diabetes Mellitus, Type 2/blood ; Female ; Humans ; Male ; Middle Aged ; United States/epidemiology
    • Abstract:
      Aim: To evaluate the extent to which balance in unmeasured characteristics of patients with type 2 diabetes (T2DM) was achieved in claims data, by comparing against more detailed information from linked electronic health records (EHR) data.
      Methods: Within a large US commercial insurance database and using a cohort design, we identified patients with T2DM initiating linagliptin or a comparator agent within class (ie, another dipeptidyl peptidase-4 inhibitor) or outside class (ie, pioglitazone or a sulphonylurea) between May 2011 and December 2012. We focused on comparators used at a similar stage of diabetes to linagliptin. For each comparison, 1:1 propensity score (PS) matching was used to balance >100 baseline claims-based characteristics, including proxies of diabetes severity and duration. Additional clinical data from EHR were available for a subset of patients. We assessed representativeness of the claims-EHR-linked subset, evaluated the balance of claims- and EHR-based covariates before and after PS-matching via standardized differences (SDs), and quantified the potential bias associated with observed imbalances.
      Results: From a claims-based study population of 166 613 patients with T2DM, 7219 (4.3%) patients were linked to their EHR data. Claims-based characteristics in the EHR-linked and EHR-unlinked patients were similar (SD < 0.1), confirming the representativeness of the EHR-linked subset. The balance of claims-based and EHR-based patient characteristics appeared to be reasonable before PS-matching and generally improved in the PS-matched population, to be SD < 0.1 for most patient characteristics and SD < 0.2 for select laboratory results and body mass index categories, which was not large enough to cause meaningful confounding.
      Conclusion: In the context of pharmacoepidemiological research on diabetes therapy, choosing appropriate comparison groups paired with a new-user design and 1:1 PS matching on many proxies of diabetes severity and duration improves balance in covariates typically unmeasured in administrative claims datasets, to the extent that residual confounding is unlikely.
      (© 2017 John Wiley & Sons Ltd.)
    • References:
      N Engl J Med. 2013 Oct 3;369(14):1317-26. (PMID: 23992601)
      N Engl J Med. 2016 Jul 28;375(4):311-22. (PMID: 27295427)
      Pharmacoepidemiol Drug Saf. 2010 Aug;19(8):858-68. (PMID: 20681003)
      Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:69-80. (PMID: 22552982)
      Am J Epidemiol. 2003 Nov 1;158(9):915-20. (PMID: 14585769)
      Basic Clin Pharmacol Toxicol. 2006 Mar;98(3):253-9. (PMID: 16611199)
      Diabetologia. 2014 Nov;57(11):2237-50. (PMID: 25212258)
      N Engl J Med. 2015 Dec 3;373(23):2247-57. (PMID: 26630143)
      N Engl J Med. 2013 Oct 3;369(14):1327-35. (PMID: 23992602)
      Pharmacoepidemiol Drug Saf. 2008 Mar;17(3):297-305. (PMID: 18215000)
      Ann Intern Med. 1997 Oct 15;127(8 Pt 2):757-63. (PMID: 9382394)
      Clin Pharmacol Ther. 2007 Aug;82(2):143-56. (PMID: 17554243)
      N Engl J Med. 2015 Jul 16;373(3):232-42. (PMID: 26052984)
      Clin Pharmacol Ther. 2016 Mar;99(3):262-5. (PMID: 26659268)
      Ann Intern Med. 2004 Dec 21;141(12):929-37. (PMID: 15611490)
      N Engl J Med. 2015 Nov 26;373(22):2117-28. (PMID: 26378978)
      Drug Saf. 2015 Mar;38(3):295-310. (PMID: 25761856)
      Stat Med. 2009 Nov 10;28(25):3083-107. (PMID: 19757444)
      Epidemiology. 2009 Jul;20(4):512-22. (PMID: 19487948)
      J Clin Epidemiol. 2013 Aug;66(8 Suppl):S1-4. (PMID: 23849143)
      J Clin Epidemiol. 1993 Oct;46(10):1075-9; discussion 1081-90. (PMID: 8410092)
      Pharmacoepidemiol Drug Saf. 2006 May;15(5):291-303. (PMID: 16447304)
      J Clin Epidemiol. 2005 Apr;58(4):323-37. (PMID: 15862718)
    • Grant Information:
      K08 AG055670 United States AG NIA NIH HHS
    • Contributed Indexing:
      Keywords: administrative data; electronic medical records; glucose-lowering medications; linkage; type 2 diabetes
    • Accession Number:
      0 (Blood Glucose)
      0 (Hypoglycemic Agents)
      3X29ZEJ4R2 (Linagliptin)
    • Publication Date:
      Date Created: 20171206 Date Completed: 20190111 Latest Revision: 20220318
    • Publication Date:
      20240628
    • Accession Number:
      PMC6207375
    • Accession Number:
      10.1111/dom.13184
    • Accession Number:
      29206336