Efficient logistic regression designs under an imperfect population identifier

Biometrics

Volumn 70, Issue 1, 2014, Pages 175-184

  Albert, Paul S ^a   Liu, Aiyi ^a   Nansel, Tonja ^a  

^a EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

Author keywords

Case control designs; Diagnostic accuracy; Epidemiologic designs; Measurement error; Misclassification

Indexed keywords

OPTIMAL SYSTEMS; REGRESSION ANALYSIS;

CASE-CONTROL; CASE-CONTROL DESIGN; CONTROL DESIGN; DIAGNOSTIC ACCURACY; EPIDEMIOLOGIC DESIGN; IMPERFECT TESTS; LOGISTICS REGRESSIONS; MISCLASSIFICATIONS; OPTIMAL DESIGN; STUDY DESIGN;

MAXIMUM LIKELIHOOD ESTIMATION;

COMPARATIVE STUDY; DIABETES; EPIDEMIOLOGY; ERROR ANALYSIS; HUMAN BEHAVIOR; OPTIMIZATION; PARAMETERIZATION; POPULATION CHARACTERISTICS; REGRESSION ANALYSIS; SAMPLING;

GLYCOSYLATED HEMOGLOBIN;

ADOLESCENT; ARTICLE; BEHAVIOR THERAPY; CASE-CONTROL DESIGNS; CHILD; COMPUTER SIMULATION; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; EPIDEMIOLOGIC DESIGNS; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; MEASUREMENT ERROR; METHODOLOGY; MISCLASSIFICATION; PSYCHOLOGICAL ASPECT; STANDARD; STATISTICAL MODEL;

CASE-CONTROL DESIGNS; DIAGNOSTIC ACCURACY; EPIDEMIOLOGIC DESIGNS; MEASUREMENT ERROR; MISCLASSIFICATION;

ADOLESCENT; BEHAVIOR THERAPY; CHILD; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 1; DIAGNOSTIC ERRORS; HEMOGLOBIN A, GLYCOSYLATED; HUMANS; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; RESEARCH DESIGN;

EID: 84895868643     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12106     Document Type: Article

References (21)

1. Alonzo, T. A., Pepe, M. S., and Lumley, T. (2003). Estimating disease prevalence in two-phase studies. Biostatistics 4, 313-326.
2. Alonzo, T. A. and Pepe, M. S. (2005). Assessing accuracy of a continuous screening test in the presence of verification bias. Journal of the Royal Statistical Society, Series C 54, 173-190.
3. Brenner, H. and Gefeller, O. (1993). Use of the positive predictive value to correct for disease misclassification in epidemiologic studies. American Journal of Epidemiology 136, 1007-1015.
4. Breslow, N. E. and Holubkov, R. (1997). Maximum-likelihood estimation of logistic regression parameters under two-phase, outcome-dependent sampling. Journal of the Royal Statistical Society, Series B 59, 447-461.
5. Duffy, S. W., Warwick, J., Williams, A. R. W., Keshavarz, H., Kaffashian, F., Rohan, T. E., Nili, F., and Sadeghi-Hassanabadi, A. (2003). A simple model for potential use with a misclassified binary outcome in epidemiology. Journal of Epidemiology and Community Health 58, 712-717.
6. Espeland, M. A. and Hui, S. L. (1987). A general approach to analyzing epidemiologic data that contains misclassification errors. Biometrics 43, 1001-1012.
7. Follmann, D. A. On the effect of treatment among would-be treatment compliers: An analysis of the multiple risk factor intervention trial. Journal of the American Statistical Association 95, 1101-1109.
8. Kuchenhoff, H., Mwalili, S. M., and Lesaffre, E. (2006). A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics 62, 85-96.
9. Kuk, A. Y., Li, J., and Rush, A. J. (2010). Recursive subsetting to identify patients in the STAR*D: A method to enhance the accuracy of early prediction of treatment outcome and to inform personalized care. Journal of Clinical Psychiatry 71, 1502-1508.
10. Mathew, T. and Sinha, B. K. (2001). Optimal designs for binary data under logistic regression. Journal of Statistical Planning and Inference 93, 295-307.
11. Morrissey, M. and Spiegelman, D. (1999). Matrix method for estimationg odds ratios with misclassified exposure data: extensions and comparisons. Biometrics 55, 338-344.
12. Nansel, T., Iannotti, R., and Liu, A. (2012). Clinical-integrated behaviorial intervention for families of youth with Type I diabetes: randomized clinical trial. Pediatrics 129, e866-e873.
13. Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association 33, 101-116.
14. Prentice, R. L. and Pyke, R. (1979). Logistic disease incidence models and case-control studies. Biometrika 66, 403-411.
15. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846-866.
16. Rose, S. and van der Laan, M. J. (2011). A targeted maximum-likelihood estimator for two-stage designs. The International Journal of Biostatistics 7, Article 17.
17. Sansbury, L. B., Wanke, K., Albert, P. S., Kahle, L., Schatzkin, A., and Lanza, E. (2090). The effect of strict adherence to a high-fiber, high-fruit and -vegetable, and low-fat eating pattern on adenoma recurrence. American Journal of Epidemiology 170, 576-584.
18. Sitter, R. R. and Wu, C. F. J. (1993). Optimal designs for binary response experiments: Fieller, D, and A criteria. Scandinavian Journal of Statistics 20, 329-341.
19. Thisted, R. A. (1988). Elements of Statistical Computing: Numerical Computation. New York, NY: Chapman and Hall.
20. The Juvenile Diabetes Research Foundation Continuous Monitoring Study Group. (2008). Continuous glucose monitoring and intensive treatment of type I diabetes. New England Journal of Medicine 359, 1464-1475.
21. Wolfram, S. (1999). Mathematica: A System for Doing Mathematics by Computer, 2nd edition. New York, NY: Addison-Wesley.