Pre-processing data to reduce biases: full matching incorporating an instrumental variable in population-based studies

Abstract

Abstract Background Epidemiologists are often concerned with unobserved biases that produce confounding in population-based studies. We introduce a new design approach—‘full matching incorporating an instrumental variable (IV)’ or ‘Full-IV Matching’—and illustrate its utility in reducing observed and unobserved biases to increase inference accuracy. Our motivating example is tailored to a central question in humanitarian emergencies—the difference in sexual violence risk by displacement setting. Methods We conducted a series of 1000 Monte Carlo simulations generated from a population-based survey after the 2010 Haitian earthquake and included earthquake damage severity as an IV and the unmeasured variable of ‘social capital’. We compared standardized mean differences (SMDs) for covariates after different designs to understand potential biases. Mean risk differences (RDs) were used to assess each design’s accuracy in estimating the oracle of the simulated data set. Results Naive analysis and pair matching equivalently performed. Full matching reduced imbalances between exposed and comparison groups across covariates, except for the unobserved covariate of ‘social capital’. Pair and full matching overstated differences in sexual violence risk when displaced to a camp vs a community [pair: RD = 0.13, 95% simulation interval (SI) 0.09–0.16; full: RD = 0.11, 95% SI 0.08–0.14). Full-IV Matching reduced imbalances across observed covariates and importantly ‘social capital’. The estimated risk difference (RD = 0.07, 95% SI 0.03–0.11) was closest to the oracle (RD = 0.06, 95% SI 0.4–0.8). Conclusion Full-IV Matching is a novel approach that is promising for increasing inference accuracy when unmeasured sources of bias likely exist.