An investigation of the MC-SIMEX method with application to measurement error in periodontal outcomes

• Published in: Statistics in medicine Vol. 28; no. 28; pp. 3523 - 3538
• Main Authors: Slate, Elizabeth H., Bandyopadhyay, Dipankar
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 10.12.2009; Wiley Subscription Services, Inc
• Subjects: Algorithms; clustered data; Computer Simulation; Cross-Sectional Studies; Cytokines - analysis; Dental care; Female; Gingival Crevicular Fluid - immunology; Gum disease; Humans; Male; measurement error; Measurement errors; Medical research; Medical statistics; misclassification; Models, Immunological; Models, Statistical; periodontal disease; Periodontal Diseases - immunology; SIMEX
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.3656

Measurement error is pervasive in medical research. In periodontal research studies, one measure of disease status is the probed pocket depth (PPD), the depth of the space between a tooth and the surrounding gum. In larger studies, these assessments are made by multiple examiners, each having distinct measurement error characteristics. Because PPD is recorded in whole millimeters, it may be regarded as discrete and its associated error as misclassification error. This study investigates the impact of this measurement error when evaluating the effect of periodontal disease status on levels of inflammatory markers in gingival crevicular fluid (GCF). The marker readings are either left or right censored, due to quantities that are either too small to be reliably quantified or so large that they saturate the detector. Additionally, marker readings from multiple periodontal sites within a subject's mouth are correlated. These considerations give rise to a clustered survival model for the marker readings in which the discrete predictor of interest is misclassified. Associations between the GCF markers and periodontal assessments are corrected for misclassification error using the MC‐SIMEX method. Simulation studies reveal the impact of varying degrees of misclassification error on associations of interest. Analysis of pilot data from a periodontal study, for which examiner misclassification rates are estimated from calibration studies, further illustrates the approach. Copyright © 2009 John Wiley & Sons, Ltd.