Details

Autor(en) / Beteiligte

• Patorno, Elisabetta
• Gopalakrishnan, Chandrasekar
• Franklin, Jessica M.
• Brodovicz, Kimberly G.
• Masso‐Gonzalez, Elvira
• Bartels, Dorothee B.
• Liu, Jun
• Schneeweiss, Sebastian

Titel

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

Ist Teil von

• Diabetes, obesity & metabolism, 2018-04, Vol.20 (4), p.974-984

Ort / Verlag

Oxford, UK: Blackwell Publishing Ltd

Erscheinungsjahr

2018

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• 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.