A Calibrated Sensitivity Analysis for Matched Observational Studies with Application to the Effect of Second-Hand Smoke Exposure on Blood Lead Levels in Children. Issue 5 (28th August 2020)

• Record Type: Journal Article
• Authors: Zhang, Bo; Small, Dylan S.
• Is Part Of: Journal of the Royal Statistical Society. Volume 69:Issue 5(2020)
• Page Start: 1285
• Page End: 1305
• Publication Date: 2020-08-28
• Subjects: Calibration -- Matched observational studies -- Second-hand smoke -- Sensitivity analysis; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12443
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: We conducted a matched observational study to investigate the causal relationship between second-hand smoke and blood lead levels in children. Our first analysis that assumes no unmeasured confounding suggests evidence of a detrimental effect of second-hand smoke. However, unmeasured confounding is a concern in our study as in other observational studies of second-hand smoke's effects. A sensitivity analysis asks how sensitive the conclusion is to a hypothesized unmeasured confounder U . For example, in our study, one potential unmeasured confounder is whether the child attends a public or private school. A commonly used sensitivity analysis for matched observational studies adopts a worst-case perspective, which assumes that, in each matched set, the unmeasured confounder is allocated to make the bias worst: in a matched pair, the child with higher blood lead level always attends public school and the other private school. This worst-case allocation of U does not correspond to any realistic distribution of U in the population and is difficult to compare with observed covariates. We proposed a new sensitivity analysis method that addresses these concerns. We apply the new method to our study and find that, to explain away the association between second-hand smoke exposure and blood lead levels as non-causal, the unmeasured confounder would have to be a bigger confounder than any measured confounder.