title: Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.
abstract: Our purpose was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data 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).
  
  
  
  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.
  
  
  
  Multi-site studies must consider site variability in missing data. Different missing data methods performed similarly. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.|Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, CO, USA.
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.affiliation: Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Shetterly
authors.affiliation: Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
authors.forename: Christine Y
authors.initials: CY
authors.lastname: Lu
authors.affiliation: Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
authors.forename: James
authors.initials: J
authors.lastname: Flory
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gagne
authors.affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors.forename: Frank E
authors.initials: FE
authors.lastname: Harrell
authors.affiliation: HealthCore, Wilmington, DE, USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Haynes
authors.affiliation: Kaiser Permanente Northern California, Oakland, CA, USA.
authors.forename: Lisa J
authors.initials: LJ
authors.lastname: Herrinton
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Elisabetta
authors.initials: E
authors.lastname: Patorno
authors.affiliation: Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Popovic
authors.affiliation: Comprehensive Health Insights, Humana Inc., Louisville, KY, USA.
authors.forename: Mano
authors.initials: M
authors.lastname: Selvan
authors.affiliation: Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Azadeh
authors.initials: A
authors.lastname: Shoaibi
authors.affiliation: Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Xingmei
authors.initials: X
authors.lastname: Wang
authors.affiliation: Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Roy
mesh_terms: D014150:Antipsychotic Agents / Q000008:administration & dosage / Q000009:adverse effects; D019411:Clinical Laboratory Techniques; D015986:Confounding Factors, Epidemiologic; D003404:Creatinine / Q000032:analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D005947:Glucose / Q000032:analysis; D006801:Humans; D019934:International Normalized Ratio / Q000379:methods; D017706:Lisinopril / Q000008:administration & dosage / Q000009:adverse effects; D008297:Male; D016016:Proportional Hazards Models; D012044:Regression Analysis; D014859:Warfarin / Q000008:administration & dosage / Q000009:adverse effects
chemical_list: D014150:Antipsychotic Agents; D014859:Warfarin; D003404:Creatinine; D017706:Lisinopril; D005947:Glucose
keywords: Mini-Sentinel; baseline confounders; database; laboratory test results; missing data methods; observational data; pharmacoepidemiology