ArticleGlobal epidemiology2021
Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation.
Article in Global epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparison of two measurement error-correction approaches for assessing the association between choline intake and coronary heart disease prevalence among US community-dwelling adults.Annals of epidemiology · 2025Article
- Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation.Global epidemiology · 2021Article
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Authors and funding
2 authors.
Funding
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Abstract
Objective: Sensitivity analysis for random measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been compared for this purpose. Study design and setting: A simulation study was conducted comparing the performance of regression calibration and simulation-extrapolation for linear and logistic regression. The performance of the two methods was evaluated in terms of bias, mean squared error (MSE) and confidence interval coverage, for various values of reliability of the error-prone measurement (0.05-0.91), sample size (125-4000), number of replicates (2-10), and R-squared (0.03-0.75). It was assumed that no validation data were available about the error-free measures, while correct information about the measurement error variance was available. Results: Regression calibration was unbiased while simulation-extrapolation was biased: median bias was 0.8% (interquartile range (IQR): -0.6;1.7%), and -19.0% (IQR: -46.4;-12.4%), respectively. A small gain in efficiency was observed for simulation-extrapolation (median MSE: 0.005, IQR: 0.004;0.006) versus regression calibration (median MSE: 0.006, IQR: 0.005;0.009). Confidence interval coverage was at the nominal level of 95% for regression calibration, and smaller than 95% for simulation-extrapolation (median coverage: 85%, IQR: 73;93%). The application of regression calibration and simulation-extrapolation for a sensitivity analysis was illustrated using an example of blood pressure and kidney function. Conclusion: Our results support the use of regression calibration over simulation-extrapolation for sensitivity analysis for random measurement error.
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