title: Exploration of heterogeneity in distributed research network drug safety analyses.
abstract: Distributed data networks representing large diverse populations are an expanding focus of drug safety research. However, interpreting results is difficult when treatment effect estimates vary across datasets (i.e., heterogeneity). In a previous study, risk estimates were generated for selected drugs and potential adverse outcomes. Analyses were replicated across eight distributed data sources using an identical analytic structure. To evaluate heterogeneity of risk estimates across data sources, the estimates were combined with summary-level data characterizing the population of each data source. Meta-analysis, meta-regression, and plots of the influence on overall results versus contribution to heterogeneity were examined and used to illustrate an approach to heterogeneity assessment. Heterogeneity, as measured by the I-squared statistic, was high with variability across outcomes. Plots of the relationship between influence on overall results and contribution to heterogeneity suggest that certain datasets and characteristics were influential but there was variability dependent on the drug and outcome being assessed. Exploratory meta-regression identified many possible influential factors, but may be subject to ecological bias and false positive conclusions. Distributed data network drug safety analyses can produce heterogeneous risk estimates that may not be easily explained. Approaches illustrated here can be useful for research that is subject to similar problems with heterogeneity.
authors.affiliation: Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Hansen
authors.affiliation: Department of Mathematics and Statistics, College of Science and Math, Auburn University, Auburn, AL, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Zeng
authors.affiliation: Research Analytics Department, Janssen Research and Development, LLC, Titusville, NJ, USA.|Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, MD, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Ryan
authors.affiliation: Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
authors.forename: Juan
authors.initials: J
authors.lastname: Gao
authors.affiliation: Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
authors.forename: Kalyani
authors.initials: K
authors.lastname: Sonawane
authors.affiliation: Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Teeter
authors.affiliation: National Pharmaceutical Council (NPC), Washington, DC, USA.
authors.forename: Kimberly
authors.initials: K
authors.lastname: Westrich
authors.affiliation: National Pharmaceutical Council (NPC), Washington, DC, USA.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Dubois
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data; D015982:Bias; D003627:Data Interpretation, Statistical; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D008498:Medical Record Linkage / Q000379:methods*; D015201:Meta-Analysis as Topic; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity
keywords: distributed data; drug safety; heterogeneity; meta-analysis; meta-regression