Flexible Approach to Measurement Error Correction in Case-Control Studies
We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of t...
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| Published in | Biometrics Vol. 64; no. 4; pp. 1207 - 1214 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.12.2008
Blackwell Publishing Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0006-341X 1541-0420 1541-0420 |
| DOI | 10.1111/j.1541-0420.2008.00999.x |
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| Abstract | We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. |
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| AbstractList | Summary We investigate the use of prospective likelihood methods to analyze retrospective case–control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case–control sampling scheme can be ignored if one adequately models the distribution of the error‐prone covariates in the case–control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case–control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error‐prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. [PUBLICATION ABSTRACT] We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. Summary We investigate the use of prospective likelihood methods to analyze retrospective case–control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case–control sampling scheme can be ignored if one adequately models the distribution of the error‐prone covariates in the case–control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case–control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error‐prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. /// Nous étudions l'utilisation des méthodes de vraisemblance prospective pour l'analyse rétrospective de données cas-témoin lorsque certaines de covariables sont mesurées avec erreur. Nous montrons que les méthodes prospectives peuvent être appliquées en ignorant le schéma d'échantillonnage cas-témoin si on modélise correctement la distribution des covariables sujettes à erreur dans le dispositif d'échantillonnage cas-témoin. En effet, dans ce cas, les méthodes de vraisemblance prospectives conduisent à des estimateurs consistants, avec des écarts-types asymptotiquement corrects. Cependant la distribution de telles covariables n'est pas la même dans la population et dans l'échantillonnage cas-témoin, imposant ainsi le besoin de modéliser la flexibilité de distribution. Dans cet article, nous illustrons le principe général en modélisant la distribution des covariables continues sujettes à erreur par une distribution normale asymétrisée (skewnormal). La performance de la méthode est évaluée par des études sur simulations, qui donnent des résultats satisfaisants en terme de biais et de recouvrement. Enfin, la méthodes est appliquée à l'analyse de deux jeux de données qui proviennent respectivement d'une étude sur le cholestérol et d'une étude sur le cancer du sein. We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer.SUMMARYWe investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. |
| Author | Guolo, A. |
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| References_xml | – reference: Beaton, G. H., Milner, J., and Little, J. A. (1979). Sources of variation in 24-hour dietary recall data: Implications for nutrition study design and interpretation. American Journal of Clinical Nutrition 32, 2546-2559. – reference: Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics 12, 171-178. – reference: Jones, D. Y., Schatzkin, A., Green, S. B., Block, G., Brinton, L. A., Ziegler, R. G., Hoover, R., and Taylor, P. R. (1987). Dietary fat and breast cancer in the National Health and Nutrition Survey I: Epidemiologic follow-up study. Journal of the National Cancer Institute 79, 465-471. – reference: Schafer, D. W. and Purdy, K. G. (1996). Likelihood analysis for errors-in-variables regression with replicate measurements. Biometrika 83, 813-824. – reference: Willett, W. C., Meir, J. S., Colditz, G. A., Rosner, B. A., Hennekens, C. H., and Speizer, F. E. (1987). Dietary fat and the risk of breast cancer. New England Journal of Medicine 316, 22-25. – reference: Roeder, K., Carroll, R. J., and Lindsay, B. G. (1996). A semiparametric mixture approach to case-control studies with errors in covariables. Journal of the American Statistical Association 91, 722-732. – reference: Prentice, R. L., Pepe, M., and Self, S. G. (1989). Dietary fat and breast cancer: A review of the literature and a discussion of methodologic issues. Cancer Research 49, 3147-3156. – reference: Richardson, S., Leblond, L., Jaussent, I., and Green, P. J. (2002). Mixture models in measurement error problems, with reference to epidemiological studies. Journal of the Royal Statistical Society, Series A 165, 549-566. – reference: Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. (2006). Measurement Error in Nonlinear Models: A Modern Perspective. Boca Raton , Florida : Chapman & Hall, CRC Press. – reference: Satten, G. A. and Kupper, L. L. (1993). 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| SubjectTerms | Biometric Methodology Biostatistics Breast cancer Breast Neoplasms Case control studies Cholesterol Cholesterols Confidence interval Disease models Error analysis Error rates Estimation bias Humans Likelihood Likelihood Functions Logistic regression logit analysis Measurement error Medical statistics Modeling Parametric models Regression calibration retrospective studies Retrospective study Skewnormal distribution Standard error Statistical methods |
| Title | Flexible Approach to Measurement Error Correction in Case-Control Studies |
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