Journal
ANNALS OF EPIDEMIOLOGY
Volume 26, Issue 9, Pages 605-611Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.annepidem.2016.07.009
Keywords
Bias; Confounding; Sensitivity analysis
Categories
Funding
- Netherlands Organization for Scientific Research (NWO-Veni project) [916.13.028]
- British Heart Foundation [PG/07/131/24254]
- Department of Health Policy Research Programme (England)
- UK Medical Research Council [MC_UU_12013/5, MC_UU_12013/9]
- UK NIHR Senior Investigator Awards [NF-SI-0611-10168, NF-SI-0611-10196]
- Netherlands Organization for Scientific Research (NWO-Vidi project) [917.16.430]
- Medical Research Council [MC_UU_12013/9, MC_UU_12013/5] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0611-10168] Funding Source: researchfish
- MRC [MC_UU_12013/5, MC_UU_12013/9] Funding Source: UKRI
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Purpose: Observational studies are prone to (unmeasured) confounding. Sensitivity analysis of unmeasured confounding typically focuses on a single unmeasured confounder. The purpose of this study was to assess the impact of multiple (possibly weak) unmeasured confounders. Methods: Simulation studies were performed based on parameters estimated from the British Women's Heart and Health Study, including 28 measured confounders and assuming no effect of ascorbic acid intake on mortality. In addition, 25, 50, or 100 unmeasured confounders were simulated, with various mutual correlations and correlations with measured confounders. Results: The correlated unmeasured confounders did not need to be strongly associated with exposure and outcome to substantially bias the exposure-outcome association at interest, provided that there are sufficiently many unmeasured confounders. Correlations between unmeasured confounders, in addition to the strength of their relationship with exposure and outcome, are key drivers of the magnitude of unmeasured confounding and should be considered in sensitivity analyses. However, if the unmeasured confounders are correlated with measured confounders, the bias yielded by unmeasured confounders is partly removed through adjustment for the measured confounders. Conclusions: Discussions of the potential impact of unmeasured confounding in observational studies, and sensitivity analyses to examine this, should focus on the potential for the joint effect of multiple unmeasured confounders to bias results. (C) 2016 Elsevier Inc. All rights reserved.
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