Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model

Varadan Sevilimedu; Lili Yu; Ding-Geng Chen; Y. L. Lio

doi:10.1007/978-3-031-12366-5_15

Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model

Varadan Sevilimedu, Lili Yu, Ding-Geng Chen, Y. L. Lio

Biostatistics, Epidemiology, & Environmental Health Sciences

Research output: Contribution to book or proceeding › Chapter

Abstract

Misclassification of binary covariates often occurs in survival data and any survival data analysis ignoring such misclassification will result in estimation bias. To handle such misclassification, the misclassification simulation extrapolation (MC-SIMEX) procedure is a flexible method proposed in survival data analysis, which has been investigated extensively for right-censored survival data. However, the performance of the MC-SIMEX method has not been explored enough for interval-censored survival data. This chapter is aimed to investigate the performance of the MC-SIMEX procedure to interval-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.

Original language	American English
Title of host publication	Emerging Topics in Modeling Interval-Censored Survival Data
DOIs	https://doi.org/10.1007/978-3-031-12366-5_15
State	Published - Nov 30 2022

Keywords

Measurement error
Misclassification
SIMEX
Survival analysis

DC Disciplines

Biostatistics
Epidemiology

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10.1007/978-3-031-12366-5_15License: Unspecified

https://link.springer.com/chapter/10.1007/978-3-031-12366-5_15

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title = "Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model",

abstract = " Misclassification of binary covariates often occurs in survival data and any survival data analysis ignoring such misclassification will result in estimation bias. To handle such misclassification, the misclassification simulation extrapolation (MC-SIMEX) procedure is a flexible method proposed in survival data analysis, which has been investigated extensively for right-censored survival data. However, the performance of the MC-SIMEX method has not been explored enough for interval-censored survival data. This chapter is aimed to investigate the performance of the MC-SIMEX procedure to interval-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.",

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author = "Varadan Sevilimedu and Lili Yu and Ding-Geng Chen and Lio, {Y. L.}",

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T1 - Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model

AU - Sevilimedu, Varadan

AU - Yu, Lili

AU - Chen, Ding-Geng

AU - Lio, Y. L.

PY - 2022/11/30

Y1 - 2022/11/30

N2 - Misclassification of binary covariates often occurs in survival data and any survival data analysis ignoring such misclassification will result in estimation bias. To handle such misclassification, the misclassification simulation extrapolation (MC-SIMEX) procedure is a flexible method proposed in survival data analysis, which has been investigated extensively for right-censored survival data. However, the performance of the MC-SIMEX method has not been explored enough for interval-censored survival data. This chapter is aimed to investigate the performance of the MC-SIMEX procedure to interval-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.

AB - Misclassification of binary covariates often occurs in survival data and any survival data analysis ignoring such misclassification will result in estimation bias. To handle such misclassification, the misclassification simulation extrapolation (MC-SIMEX) procedure is a flexible method proposed in survival data analysis, which has been investigated extensively for right-censored survival data. However, the performance of the MC-SIMEX method has not been explored enough for interval-censored survival data. This chapter is aimed to investigate the performance of the MC-SIMEX procedure to interval-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.

KW - Measurement error

KW - Misclassification

KW - SIMEX

KW - Survival analysis

UR - https://digitalcommons.georgiasouthern.edu/bee-facpubs/379

UR - https://link.springer.com/chapter/10.1007/978-3-031-12366-5_15

U2 - 10.1007/978-3-031-12366-5_15

DO - 10.1007/978-3-031-12366-5_15

M3 - Chapter

BT - Emerging Topics in Modeling Interval-Censored Survival Data

ER -

Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model

Abstract

Keywords

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