Methods for using clinical laboratory test results as baseline confounders in multi‐site observational database studies when missing data are expected. Issue 7 (4th May 2016)

• Record Type: Journal Article
• Authors: Raebel, Marsha A.; Shetterly, Susan; Lu, Christine Y.; Flory, James; Gagne, Joshua J.; Harrell, Frank E.; Haynes, Kevin; Herrinton, Lisa J.; Patorno, Elisabetta; Popovic, Jennifer; Selvan, Mano; Shoaibi, Azadeh; Wang, Xingmei; Roy, Jason
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 25:Issue 7(2016)
• Page Start: 798
• Page End: 814
• Publication Date: 2016-05-04
• Subjects: laboratory test results -- missing data methods -- baseline confounders -- observational data -- database -- Mini‐Sentinel -- pharmacoepidemiology -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4015
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: Our purpose was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data methods. Methods: Using the Mini‐Sentinel Distributed Database from three sites, we selected three exposure–outcome scenarios with laboratory results as baseline confounders. We compared hazard ratios (HRs) or risk differences (RDs) and 95% confidence intervals (CIs) from models that omitted laboratory results, included only available results (complete cases), and included results after applying missing data methods (multiple imputation [MI] regression, MI predictive mean matching [PMM] indicator). Results: Scenario 1 considered glucose among second‐generation antipsychotic users and diabetes. Across sites, glucose was available for 27.7–58.9%. Results differed between complete case and missing data models (e.g., olanzapine: HR 0.92 [CI 0.73, 1.12] vs 1.02 [0.90, 1.16]). Across‐site models employing different MI approaches provided similar HR and CI; site‐specific models provided differing estimates. Scenario 2 evaluated creatinine among individuals starting high versus low dose lisinopril and hyperkalemia. Creatinine availability: 44.5–79.0%. Results differed between complete case and missing data models (e.g., HR 0.84 [CI 0.77, 0.92] vs. 0.88 [0.83, 0.94]). HR and CI were identical across MI methods. Scenario 3 examined international normalized ratio (INR) among warfarin users starting interacting versus noninteracting antimicrobials and bleeding. INR availability: 20.0–92.9%. Results differed between ignoring INR versus including INR using missing data methods (e.g., RD 0.05 [CI −0.03, 0.13] vs 0.09 [0.00, 0.18]). Indicator and PMM methods gave similar estimates. Conclusion: Multi‐site studies must consider site variability in missing data. Different missing data methods performed similarly. Copyright © 2016 John Wiley & Sons, Ltd.