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Abstract

The problem of misclassification in covariates is ubiquitous in survival data and often leads to biased estimates. The misclassification simulation extrapolation method is a popular method to correct this bias. However, its impact on Weibull accelerated failure time models has not been studied. In this paper, we study the bias caused by misclassification in one or more binary covariates in Weibull accelerated failure time models and explore the use of the misclassification simulation extrapolation in correcting for this bias, along with its asymptotic properties. Simulation studies are carried out to investigate the numerical properties of the resulting estimator for finite samples. The proposed method is then applied to colon cancer data obtained from the cancer registry at Memorial Sloan Kettering Cancer Center.

Keywords

Accelerated failure time model measurement error misclassification survival analysis weibull distribution

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References

Dalen

Buonaccorsi

Laake

, et al. Regression analysis with categorized regression calibrated exposure: some interesting findings. Emerg Themes Epidemiol 2006; 3: 6.

Spiegelman

, et al. Functional and structural methods with mixed measurement error and misclassification in covariates. J Am Stat Assoc 2015; 110: 681–696.

Küchenhoff

Mwalili

Lesaffre

. A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics 2006; 62: 85–96.

Kauhanen

Lakka

Lynch

, et al. Social disadvantages in childhood and risk of all-cause death and cardiovascular disease in later life: a comparison of historical and retrospective childhood information. Int J Epidemiol Aug 2006; 35: 962–968.

Buonaccorsi

Laake

Veierød

. On the effect of misclassification on bias of perfectly measured covariates in regression. Biometrics Sep 2005; 61: 831–836.

Ahrens

Lash

Louik

, et al. Correcting for exposure misclassification using survival analysis with a time-varying exposure. Ann Epidemiol Nov 2012; 22: 799–806.

Zucker

Spiegelman

. Inference for the proportional hazards model with misclassified discrete-valued covariates. Biometrics Jun 2004; 60: 324–334.

Zucker

. A pseudo–partial likelihood method for semiparametric survival regression with covariate errors. J Am Stat Assoc 2005; 100: 1264–1277.

Zucker

Spiegelman

. Corrected score estimation in the proportional hazards model with misclassified discrete covariates. Stat Med 2008; 27: 1911–1933.

10.

Spiegelman

Carroll

Kipnis

. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Stat Med Jan 15 2001; 20: 139–160.

11.

Bang

Chiu

Y-L

Kaufman

, et al. Bias correction methods for misclassified covariates in the cox model: comparison of five correction methods by simulation and data analysis. J Stat Theory Pract 2013; 7: 381–400.

12.

Cole

Chu

Greenland

. Multiple-imputation for measurement-error correction. Int J Epidemiol 2006; 35: 1074–1081.

13.

Zhou

Pepe

. Auxiliary covariate data in failure time regression. Biometrika 1995; 82: 139–149.

14.

Slate

Bandyopadhyay

. An investigation of the MC-SIMEX method with application to measurement error in periodontal outcomes. Stat Med Dec 10 2009; 28: 3523–3538.

15.

Cook

Stefanski

. Simulation-extrapolation estimation in parametric measurement error models. J Am Stat Assoc 1994; 89: 1314–1328.

16.

Sevilimedu

Samawi

, et al. Application of the misclassification simulation extrapolation procedure to log-logistic accelerated failure time models in survival analysis. J Stat Theory Pract 2018; 13: 24.

17.

Liu

Lim

. Using the weibull accelerated failure time regression model to predict time to health events. bioRxiv 2018: 362186. doi:10.1101/362186

18.

Carroll

Ruppert

Stefanski

, et al. Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition. New York: CRC Press, 2006.

19.

Stefanski

Cook

. Simulation-extrapolation: the measurement error jackknife. J Am Stat Assoc 1995; 90: 1247–1256.

20.

Xiong

. Accelerated failure time models with covariates subject to measurement error. Stat Med 2007; 26: 4817–4832.

21.

Weiser

Landmann

Kattan

, et al. Individualized prediction of colon cancer recurrence using a nomogram. J Clin Oncol Jan 20 2008; 26: 380–385.

22.

Gönen

Schrag

Weiser

Nodal staging score: a tool to assess adequate staging of node-negative colon cancer. J Clin Oncol 2009; 27: 6166–6171.

23.

Delignette-Muller

Dutang

. Fitdistrplus: an R package for fitting distributions. J Stat Softw 2015; 64: 1–34.

24.

R Foundation for Statistical Computing; 2022.

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Misclassification simulation extrapolation method for a Weibull accelerated failure time model

Abstract

Keywords

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Supplementary Material