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Published in: Clinical Oral Investigations 8/2013

01-11-2013 | Review

Correction for misclassification of caries experience in the absence of internal validation data

Authors: T. Mutsvari, D. Declerck, E. Lesaffre

Published in: Clinical Oral Investigations | Issue 8/2013

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Abstract

Objectives

To quantify the effects of risk factors and/or determinants on disease occurrence, it is important that the risk factors as well as the variable that measures the disease outcome are recorded with the least error as possible. When investigating the factors that influence a binary outcome, a logistic regression model is often fitted under the assumption that the data are collected without error. However, most categorical outcomes (e.g., caries experience) are accompanied by misclassification and this needs to be accounted for. The aim of this research was to adjust for binary outcome misclassification using an external validation study when investigating factors influencing caries experience in schoolchildren.

Materials and methods

Data from the Signal Tandmobiel® study were used. A total of 500 children from the main and 148 from the validation study were included in the analysis. Regression models (with several covariates) for sensitivity and specificity were used to adjust for misclassification in the main data.

Results

The use of sensitivity and specificity modeled as functions of several covariates resulted in a better correction compared to using point estimates of sensitivity and specificity. Age, geographical location of the school to which the child belongs, dentition type, tooth type, and surface type were significantly associated with the prevalence of caries experience.

Conclusions

Sensitivity and specificity calculated based on an external validation study may resemble those obtained from an internal study if conditioned on a rich set of covariates.

Clinical relevance

Main data can be corrected for misclassification using information obtained from an external validation study when a rich set of covariates is recorded during calibration.
Appendix
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Literature
1.
go back to reference Pine CM, Pitts NB, Nugent Z (1997) British Association for the Study of Community Dentistry (BASCD) guidance on the statistical aspects of training and calibration of examiners for surveys of child dental health: a BASCD coordinated dental epidemiology programme quality standard. Community Dent Health 14(Suppl 1):18–29PubMed Pine CM, Pitts NB, Nugent Z (1997) British Association for the Study of Community Dentistry (BASCD) guidance on the statistical aspects of training and calibration of examiners for surveys of child dental health: a BASCD coordinated dental epidemiology programme quality standard. Community Dent Health 14(Suppl 1):18–29PubMed
2.
go back to reference Neuhaus JM (1999) Bias and efficiency loss due to misclassified responses in binary regression. Biometrika 86:843–855CrossRef Neuhaus JM (1999) Bias and efficiency loss due to misclassified responses in binary regression. Biometrika 86:843–855CrossRef
3.
go back to reference Neuhaus JM (2002) Analysis of clustered and longitudinal binary data subject to response misclassification. Biometrics 58:675–683PubMedCrossRef Neuhaus JM (2002) Analysis of clustered and longitudinal binary data subject to response misclassification. Biometrics 58:675–683PubMedCrossRef
4.
go back to reference Magder LS, Hughes JP (1997) Logistic regression when the outcome is measured with uncertainty. Am J Epidemiol 146(2):195–203PubMedCrossRef Magder LS, Hughes JP (1997) Logistic regression when the outcome is measured with uncertainty. Am J Epidemiol 146(2):195–203PubMedCrossRef
5.
go back to reference Mwalili S, Lesaffre E, Declerck D (2005) A Bayesian ordinal logistic regression model to correct for inter-observer measurement error in a geographical oral health study. J R Stat Soc Ser C Appl 54:77–93CrossRef Mwalili S, Lesaffre E, Declerck D (2005) A Bayesian ordinal logistic regression model to correct for inter-observer measurement error in a geographical oral health study. J R Stat Soc Ser C Appl 54:77–93CrossRef
6.
go back to reference Lesaffre E, Mwalili S, Declerck D (2004) Analysis of caries experience taking inter-observer bias and variability into account. J Dent Res 83(12):951–955PubMedCrossRef Lesaffre E, Mwalili S, Declerck D (2004) Analysis of caries experience taking inter-observer bias and variability into account. J Dent Res 83(12):951–955PubMedCrossRef
7.
go back to reference Tenenbein A (1986) A double sampling scheme for estimating from misclassified multinomial data with applications to sampling inspection. Biometrika 73:13–22CrossRef Tenenbein A (1986) A double sampling scheme for estimating from misclassified multinomial data with applications to sampling inspection. Biometrika 73:13–22CrossRef
8.
go back to reference Marshall RJ (1990) Validation study methods for estimating exposure proportions and odds ratios with misclassified data. J Clin Epidemiol 43:941–947PubMedCrossRef Marshall RJ (1990) Validation study methods for estimating exposure proportions and odds ratios with misclassified data. J Clin Epidemiol 43:941–947PubMedCrossRef
9.
go back to reference Küchenhoff H (2009) Misclassification and measurement error in oral health. In: Lesaffre E, Feine J, Leroux B, Declerck D (eds) Statistical and methodological aspects of oral health research. Wiley, New York, pp 279–290CrossRef Küchenhoff H (2009) Misclassification and measurement error in oral health. In: Lesaffre E, Feine J, Leroux B, Declerck D (eds) Statistical and methodological aspects of oral health research. Wiley, New York, pp 279–290CrossRef
10.
go back to reference Agbaje JO, Mutsvari T, Lesaffre E, Declerck D (2012) Examiner performance in calibration exercises compared with field conditions when scoring caries experience. Clin Oral Investig 16(2):481–488PubMedCrossRef Agbaje JO, Mutsvari T, Lesaffre E, Declerck D (2012) Examiner performance in calibration exercises compared with field conditions when scoring caries experience. Clin Oral Investig 16(2):481–488PubMedCrossRef
11.
go back to reference Declerck D, Lesaffre E, Leroy R, Vanobbergen J (2009) Examples from oral health epidemiology: the Signal Tandmobiel and smile for life studies. In: Lesaffre E, Feine J, Leroux B, Declerck D (eds) Statistical and methodological aspects of oral health research. Wiley, New York, pp 341–357 Declerck D, Lesaffre E, Leroy R, Vanobbergen J (2009) Examples from oral health epidemiology: the Signal Tandmobiel and smile for life studies. In: Lesaffre E, Feine J, Leroux B, Declerck D (eds) Statistical and methodological aspects of oral health research. Wiley, New York, pp 341–357
12.
go back to reference Pitts NB, Evans DJ, Pine CM (1997) British Association for the Study of Community Dentistry (BASCD) diagnostic criteria for caries prevalence surveys–1996/7. Community Dent Health 14(Suppl 1):6–9PubMed Pitts NB, Evans DJ, Pine CM (1997) British Association for the Study of Community Dentistry (BASCD) diagnostic criteria for caries prevalence surveys–1996/7. Community Dent Health 14(Suppl 1):6–9PubMed
13.
go back to reference Klein H, Palmer CE, Knutson JW (1938) Studies on dental caries. I. Dental status and dental needs of elementary school children. Public Health Rep 53:751–765CrossRef Klein H, Palmer CE, Knutson JW (1938) Studies on dental caries. I. Dental status and dental needs of elementary school children. Public Health Rep 53:751–765CrossRef
14.
go back to reference Mutsvari T (2012) Misclassification in multilevel models with applications in dental caries research, PhD Dissertation, KU Leuven Mutsvari T (2012) Misclassification in multilevel models with applications in dental caries research, PhD Dissertation, KU Leuven
15.
go back to reference Brenner H, Savitz DA (1990) The effects of sensitivity and specificity of case selection on validity, sample size, precision, and power in hospital-based case–control studies. Am J Epidemiol 132(1):181–192PubMed Brenner H, Savitz DA (1990) The effects of sensitivity and specificity of case selection on validity, sample size, precision, and power in hospital-based case–control studies. Am J Epidemiol 132(1):181–192PubMed
16.
go back to reference Wacholder S, Armstrong B, Hartge P (1993) Validation studies using an alloyed gold standard. Am J Epidemiol 137:1251–1258PubMed Wacholder S, Armstrong B, Hartge P (1993) Validation studies using an alloyed gold standard. Am J Epidemiol 137:1251–1258PubMed
17.
go back to reference McInturf P, Johnson WO, Cowling D, Gardner IA (2004) Modelling risk when binary outcomes are subject to error. Stat Med 23:1095–1109CrossRef McInturf P, Johnson WO, Cowling D, Gardner IA (2004) Modelling risk when binary outcomes are subject to error. Stat Med 23:1095–1109CrossRef
18.
go back to reference Ralph BD (1998) Propensity score methods for bias reduction in the comparison of treatment to non-randomized control group. Stat Med 17:2265–2281CrossRef Ralph BD (1998) Propensity score methods for bias reduction in the comparison of treatment to non-randomized control group. Stat Med 17:2265–2281CrossRef
19.
go back to reference Gustafson P (2004) Measurement error and misclassification in statistics and epidemiology: impacts and Bayesian adjustments. Chapman & Hall, London Gustafson P (2004) Measurement error and misclassification in statistics and epidemiology: impacts and Bayesian adjustments. Chapman & Hall, London
20.
go back to reference Greenland S (1980) The effects of misclassification in the presence of covariates. Am J Epidemiol 112:564–569PubMed Greenland S (1980) The effects of misclassification in the presence of covariates. Am J Epidemiol 112:564–569PubMed
21.
22.
go back to reference Plummer M (2011) JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Hornik K, Leisch F, Zeileis A (eds) Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC, 2003). Technische Universitaet Wien, Vienna, Austria. http://www.ci.tuwien.ac.at/Conferences/DSC.html. Accessed 24 Nov 2011 Plummer M (2011) JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Hornik K, Leisch F, Zeileis A (eds) Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC, 2003). Technische Universitaet Wien, Vienna, Austria. http://​www.​ci.​tuwien.​ac.​at/​Conferences/​DSC.​html. Accessed 24 Nov 2011
23.
go back to reference Plummer M, Best N, Cowles K, Vines K (2008) CODA: output analysis and diagnostics for MCMC. R Package version 0.13-3 Plummer M, Best N, Cowles K, Vines K (2008) CODA: output analysis and diagnostics for MCMC. R Package version 0.13-3
Metadata
Title
Correction for misclassification of caries experience in the absence of internal validation data
Authors
T. Mutsvari
D. Declerck
E. Lesaffre
Publication date
01-11-2013
Publisher
Springer Berlin Heidelberg
Published in
Clinical Oral Investigations / Issue 8/2013
Print ISSN: 1432-6981
Electronic ISSN: 1436-3771
DOI
https://doi.org/10.1007/s00784-013-0993-4

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