{"title":"随机测量误差的回归校正和模拟外推敏感性分析。","authors":"Linda Nab , Rolf H.H. Groenwold","doi":"10.1016/j.gloepi.2021.100067","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>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.</p></div><div><h3>Study design and setting</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>Our results support the use of regression calibration over simulation-extrapolation for sensitivity analysis for random measurement error.</p></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446124/pdf/","citationCount":"4","resultStr":"{\"title\":\"Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation\",\"authors\":\"Linda Nab , Rolf H.H. Groenwold\",\"doi\":\"10.1016/j.gloepi.2021.100067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>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.</p></div><div><h3>Study design and setting</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>Our results support the use of regression calibration over simulation-extrapolation for sensitivity analysis for random measurement error.</p></div>\",\"PeriodicalId\":36311,\"journal\":{\"name\":\"Global Epidemiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446124/pdf/\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Epidemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590113321000213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590113321000213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.