Joint model for a diagnostic test without a gold standard in the presence of a dependent terminal event

Statistics in Medicine

Volumn 33, Issue 15, 2014, Pages 2554-2566

  Luo, Sheng ^a   Su, Xiao ^a   Desantis, Stacia M ^a   Huang, Xuelin ^b   Yi, Min ^b   Hunt, Kelly K ^b  

^a UNIVERSITY OF TEXAS   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Binomial regression; Cox model; Frailty model; Informative censoring; Latent class model; Tumor relapse

Indexed keywords

ARTICLE; BINOMIAL DISTRIBUTION; BREAST CANCER; CANCER RECURRENCE; CONTROLLED STUDY; GOLD STANDARD; HUMAN; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; PROBABILITY; REGRESSION ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; SURVIVAL TIME; TERMINAL DISEASE; BREAST NEOPLASMS; COMPUTER SIMULATION; DIAGNOSIS; DIAGNOSTIC TEST; FEMALE; KAPLAN MEIER METHOD; PROCEDURES; TUMOR RECURRENCE;

BREAST NEOPLASMS; COMPUTER SIMULATION; DIAGNOSTIC TESTS, ROUTINE; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; MODELS, STATISTICAL; NEOPLASM RECURRENCE, LOCAL;

EID: 84901949050     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6101     Document Type: Article

References (55)

1. Carroll RJ, Ruppert D, Stefanski L, Crainiceanu C. Measurement Error in Nonlinear Models: A Modern Perspective. Vol. 105. Chapman and Hall/CRC: Boca Raton, FL, 2010.
2. Chen B, Grace Y, Cook R. Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random. Journal of the American Statistical Association 2010; 105(489):336-353.
3. Neuhaus J. Bias and efficiency loss due to misclassified responses in binary regression. Biometrika 1999; 86: 843-855.
4. Neuhaus J. Analysis of clustered and longitudinal binary data subject to response misclassification. Biometrics 2002; 58(3):675-683.
5. Barron B. The effects of misclassification on the estimation of relative risk. Biometrics 1977; 33: 414-418.
6. Greenland S. The effect of misclassification in matched-pair case-control studies. American Journal of Epidemiology 1982; 116: 402-406.
7. Greenland S, Ericson C, Kleinbaum D. Correcting for misclassification in two-way tables and matched-pair studies. International Journal of Epidemiology 1983; 12: 93-97.
8. Magder L, Hughes J. Logistic regression when the outcome is measured with uncertainty. American Journal of Epidemiology 1997; 146: 195-203.
9. Spiegelman D, Rosner B, Logan R. Estimation and inference for logistic regression with covariate misclassification and measurement error in main study/validation study designs. Journal of the American Statistical Association 2000; 95: 51-61.
10. Robins J, Rotnitzky A, Zhao L. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 1994; 89: 846-866.
11. Prescott G, Garthwaite P. A Bayesian approach to prospective binary outcome studies with misclassification in a binary risk factor. Statistics in Medicine 2005; 24: 3463-3477.
12. Carroll R, Ruppert D, Stefanski L, Crainiceanu C. Measurement Error in Nonlinear Models: A Modern Perspective. Chapman and Hall/CRC: Boca Raton, FL, 2006.
13. Sheps S, Schechter M. The assessment of diagnostic tests. Journal of the American Medical Association 1984; 17: 2418-2422.
14. Wacholder S, Armstrong B, Hartge P. Validation studies using an alloyed gold standard. American Journal of Epidemiology 1993; 137: 1251-1258.
15. Albert P, Dodd L. On estimating diagnostic accuracy from studies with multiple raters and partial gold standard evaluation. Journal of the American Statistical Association 2008; 103: 61-73.
16. Hui S, Zhou X. Evaluation of diagnostic tests without gold standards. Statistical Methods in Medical Research 1998; 7: 354-370.
17. Luo S, Yi M, Huang X, Hunt K. A Bayesian model for misclassified binary outcomes and correlated survival data with applications to breast cancer. Statistics in Medicine 2013; 32(13): 2320-34.
18. Rindskopf D, Rindskopf W. The value of latent class analysis in medical diagnosis. Statistics in Medicine 1986; 5: 21-27.
19. Formann A. Measurement errors in caries diagnosis: some further latent class models. Biometrics 1994; 50: 865-871.
20. Duffy S, Warwick J, Williams A, Keshavarz H, Kaffashian F, Rohan T, Nili F, Sadeghi-Hassanabadi A. A simple model for potential use with a misclassified binary outcome in epidemiology. Journal of epidemiology and community health 2004; 58(8):712-717.
21. Joseph L, Gyorkos T, Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. American Journal of Epidemiology 1995; 141(3):263-272.
22. Ren D, Stone R. A Bayesian adjustment for covariate misclassification with correlated binary outcome data. Journal of Applied Statistics 2007; 34: 1019-1034.
23. Nagelkerke N, Fidler V, Buwalda M. Instrumental variables in the evaluation of diagnostic test procedures when the true disease state is unknown. Statistics in Medicine 1988; 7: 739-44.
24. Faucett CL, Thomas DC. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Statistics in Medicine 1996; 15(15):1663-1685.
25. Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics 2000; 1(4):465-480.
26. Rouzier R, Extra J, Carton M, Falcou M, Vincent-Salomon A, Fourquet A, Pouillart P, Primary chemotherapy for operable breast cancer: incidence and prognostic significance of ipsilateral breast tumor recurrence after breast-conserving surgery. Journal of Clinical Oncology 2001; 19: 3828-35.
27. Chen A, Meric-Bernstam F, Hunt K, Thames H, Oswald M, Outlaw E, Strom E, McNeese M, Kuerer H, Ross M, Singletary S, Ames F, Feig B, Sahin A, Perkins G, Schechter N, Hortobagyi G, Buchholz T. Breast conservation after neoadjuvant chemotherapy: the MD Anderson Cancer Center experience. Journal of Clinical Oncology 2004; 22: 2303-12.
28. Huang E, Buchholz T, Meric F, Krishnamurthy S, Mirza N, Ames F, Feig B, Kuerer H, Ross M, Singletary S, McNeese M, Strom E, Hunt K. Classifying local disease recurrences after breast conservation therapy based on location and histology: new primary tumors have more favorable outcomes than true local disease recurrences. Cancer 2002; 95: 2059-67.
29. Komoike Y, Akiyama F, Iino Y, Ikeda T, Tanaka-Akashi S, Ohsumi S, Kusama M, Sano M, Shin E, Suemasu K, Sonoo H, Taguchi T, Nishi T, Nishimura R, Haga S, Mise K, Kinoshita T, Murakami S, Yoshimoto M, Tsukuma H, Inaji H. Analysis of ipsilateral breast tumor recurrences after breast-conserving treatment based on the classification of true recurrences and new primary tumors. Breast Cancer 2005; 12: 104-11.
30. Abd-Alla H, Lotayef M, AbouBakr A, Moneer M. Ipsilateral in-breast tumor relapse after breast conservation therapy: true recurrence versus new primary tumor. Journal of the Egyptian National Cancer Institute 2006; 18: 183-90.
31. Liu L, Conaway M, Knaus W, Bergin J. A random effects four-part model, with application to correlated medical costs. Computational Statistics & Data Analysis 2008; 52(9):4458-4473.
32. Liu L, Huang X. Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2009; 58(1):65-81.
33. Liu L, Strawderman R, Cowen M, Shih Y. A flexible two-part random effects model for correlated medical costs. Journal of Health Economics 2010; 29(1):110-123.
34. Lesaffre E, Spiessens B. On the effect of the number of quadrature points in a logistic random effects model: an example. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2001; 50(3):325-335.
35. Lawless J, Zhan M. Analysis of interval-grouped recurrent-event data using piecewise constant rate functions. Canadian Journal of Statistics 1998; 26(4):549-565.
36. Feng S, Wolfe R, Port F. Frailty survival model analysis of the national deceased donor kidney transplant dataset using Poisson variance structures. Journal of the American Statistical Association 2005; 100(471):728-735.
37. Liu L, Huang X, O'Quigley J. Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data. Biometrics 2008; 64(3):950-958.
38. He B, Luo S. Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease. Statistical Methods in Medical Research 2013. published online on April 16, 2013, PMCID: PMC3883896, DOI: 10.1177/0962280213480877.
39. Singletary S, Allred C, Ashley P, Bassett L, Berry D, Bland K, Borgen P, Clark G, Edge S, Hayes D, Hughes L, Hutter R, Morrow M, Page D, Recht A, Theriault R, Thor A, Weaver D, Wieand H, Greene F. Revision of the American Joint Committee on Cancer staging system for breast cancer. Journal of Clinical Oncology 2002; 20(17):3576-7.
40. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974; 19(6):716-723.
41. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 1978; 6(2):461-464.
42. Smith T, Lee D, Turner B, Carter D, Haffty B. True recurrence vs. new primary ipsilateral breast tumor relapse: an analysis of clinical and pathologic differences and their implications in natural history, prognoses, and therapeutic management. International Journal of Radiation Oncology Biology Physics 2000; 48(5):1281-1289.
43. Albert PS, Dodd LE. A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard. Biometrics 2004; 60(2):427-435.
44. Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 2001; 57(1):158-167.
45. Georgiadis M, Johnson W, Gardner I, Singh R. Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2003; 52(1):63-76.
46. Jones G, Johnson W, Hanson T, Christensen R. Identifiability of models for multiple diagnostic testing in the absence of a gold standard. Biometrics 2010; 66(3):855-863.
47. Paulino C, Soares P, Neuhaus J. Binomial regression with misclassification. Biometrics 2003; 59(3):670-675.
48. Hogan JW, Laird NM. Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 1997; 16: 239-257.
49. Hogan JW, Laird NM. Model-based approaches to analysing incomplete longitudinal and failure time data. Statistics in Medicine 1997; 16: 259-272.
50. Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. Springer Verlag: New York, 2005.
51. Zhang S, Müller P, Do KA. A Bayesian semiparametric survival model with longitudinal markers. Biometrics 2010; 66(2):435-443.
52. Song X, Davidian M, Tsiatis AA. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics 2002; 58(4):742-753.
53. Zeng D, Cai J. Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time. The Annals of Statistics 2005; 33(5):2132-2163.
54. Heagerty PJ, Kurland BF. Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika 2001; 88(4):973.
55. Daniels MJ, Zhao YD. Modelling the random effects covariance matrix in longitudinal data. Statistics in Medicine 2003; 22(10):1631-1647.