Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation

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 perfo...

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Autores principales:	Linda Nab, Rolf H.H. Groenwold
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Classical measurement error Sensitivity analysis Quantitative bias analysis Regression calibration Simulation-extrapolation Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/6f03e7afb3a244c2887462977df040fd
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spelling	oai:doaj.org-article:6f03e7afb3a244c2887462977df040fd2021-12-02T05:03:36ZSensitivity analysis for random measurement error using regression calibration and simulation-extrapolation2590-113310.1016/j.gloepi.2021.100067https://doaj.org/article/6f03e7afb3a244c2887462977df040fd2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2590113321000213https://doaj.org/toc/2590-1133Objective: 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.Linda NabRolf H.H. GroenwoldElsevierarticleClassical measurement errorSensitivity analysisQuantitative bias analysisRegression calibrationSimulation-extrapolationInfectious and parasitic diseasesRC109-216ENGlobal Epidemiology, Vol 3, Iss , Pp 100067- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Classical measurement error Sensitivity analysis Quantitative bias analysis Regression calibration Simulation-extrapolation Infectious and parasitic diseases RC109-216
spellingShingle	Classical measurement error Sensitivity analysis Quantitative bias analysis Regression calibration Simulation-extrapolation Infectious and parasitic diseases RC109-216 Linda Nab Rolf H.H. Groenwold Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
description	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.
format	article
author	Linda Nab Rolf H.H. Groenwold
author_facet	Linda Nab Rolf H.H. Groenwold
author_sort	Linda Nab
title	Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
title_short	Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
title_full	Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
title_fullStr	Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
title_full_unstemmed	Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
title_sort	sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6f03e7afb3a244c2887462977df040fd
work_keys_str_mv	AT lindanab sensitivityanalysisforrandommeasurementerrorusingregressioncalibrationandsimulationextrapolation AT rolfhhgroenwold sensitivityanalysisforrandommeasurementerrorusingregressioncalibrationandsimulationextrapolation
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Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation

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