Higher-order evidence

被引:0
作者
Stephen R. Cole
Bonnie E. Shook-Sa
Paul N. Zivich
Jessie K. Edwards
David B. Richardson
Michael G. Hudgens
机构
[1] University of North Carolina at Chapel Hill,Department of Epidemiology
[2] University of North Carolina at Chapel Hill,Department of Biostatistics
[3] University of North Carolina at Chapel Hill,Department of Medicine
[4] University of California Irvine,Department of Epidemiology
来源
European Journal of Epidemiology | 2024年 / 39卷
关键词
Bias; Error; Measurement; Misclassification; Statistics; Validation data;
D O I
暂无
中图分类号
学科分类号
摘要
Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.
引用
收藏
页码:1 / 11
页数:10
相关论文
共 27 条
[1]  
Christensen D(2010)Higher-order evidence Philos Phenomenol Res 81 185-215
[2]  
Greenland S(1996)Basic methods for sensitivity analysis of biases Int J Epidemiol 25 1107-1116
[3]  
Spiegelman D(2010)Approaches to uncertainty in exposure assessment in environmental epidemiology Annu Rev Public Health 31 149-163
[4]  
Keogh RH(2020)STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment Stat Med 39 2197-2231
[5]  
Shaw PA(2020)STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics Stat Med 39 2232-2263
[6]  
Gustafson P(2009)Bayesian perspectives for epidemiologic research: III Bias analysis via missing-data methods Int J Epidemiol 38 1662-1673
[7]  
Shaw PA(2023)Illustration of 2 Fusion Designs and Estimators Am J Epidemiol 192 467-474
[8]  
Gustafson P(1990)Polish original by Dabrowska DM and Speed T On the application of probability theory to agricultural experiments: essay on principles. Section 9. Translated from 1923 Polish original by Dabrowska DM and Speed TP Stat Sci 1990 465-472
[9]  
Carroll RJ(1978)Estimating prevalence from the results of a screening test Am J Epidemiol 107 71-76
[10]  
Greenland S(1998)Asymptotics of estimating equations under natural conditions J Multivar Anal 65 245-260