Sensitivity analysis of selection bias: a graphical display by bias-correction index

Background In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2023-11, Vol.11, p.e16411-e16411, Article e16411
Main Authors: Chung, Ping-Chen, Lin, I-Feng
Format: Article
Language:English
Subjects:
Analysis
Bias
Bias-correction
Estimates
Health survey
Influence
Informed consent (Medical law)
Methods
National health insurance
Observational study
Parameter estimation
Probability
Regression analysis
Selection bias
Sensitivity analysis
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Summary:Background In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias. Methods We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked. Results We included 15,247 people aged [greater than or equal to]20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias. Conclusions When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.16411