Regression models for method comparison data.
Regression methods for the analysis of paired measurements produced by two fallible assay methods are described and their advantages and pitfalls discussed. The difficulties for the analysis, as in any errors-in-variables problem lies in the lack of identifiability of the model and the need to introduce questionable and often naïve assumptions in order to gain identifiability. Although not a panacea, the use of instrumental variables and associated instrumental variable (IV) regression methods in this area of application has great potential to improve the situation. Large samples are frequently needed and two-phase sampling methods are introduced to improve the efficiency of the IV estimators.
Dunn
Graham
G
Biostatistics Group, Division of Epidemiology & Health Sciences, University of Manchester, Manchester, UK. graham.dunn@manchester.ac.uk
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D015203:Reproducibility of Results