Evaluation of SIMEX Extrapolation Methods in Accelerated Failure Time Models With Covariate Measurement Error.
Location
Jiann-Ping Hsu College of Public Health (JPHCOPH)
Session Format
Poster Presentation
Co-Presenters and Faculty Mentors or Advisors
Dr. Lili Yu, Faculty Advisor
Abstract
It is well known that ignoring measurement errors in covariates in the model leads to biased estimates. Various methods were proposed to address this issue. Simulation and extrapolation (SIMEX) method developed by (He et al. 2007) is a popular method due to its flexibility. It consists of two steps, simulation step and extrapolation step. Although it was investigated for applying to different settings, very little research was done on finding the best extrapolation function.. The objective of this study is to evaluate which extrapolation function gives best estimation in AFT models for data follows Weibull distribution. We use simulation studies to investigate the performance of three most popular extrapolation functions used for the SIMEX method. The results show linear extrapolation function is best for data with small measurement error, quadratic extrapolation function is best for the data with medium measurement error and nonlinear extrapolation function is best for the data with large measurement error. Then we applied these three extrapolation functions to a real data set patients with advanced lung cancer from the North Central Cancer Treatment Group to illustrate the usefulness of the research.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (Open Access)
Recommended Citation
Modi, Roshni, "Evaluation of SIMEX Extrapolation Methods in Accelerated Failure Time Models With Covariate Measurement Error." (2021). GS4 Georgia Southern Student Scholars Symposium. 26.
https://digitalcommons.georgiasouthern.edu/research_symposium/2021/2021/26
Evaluation of SIMEX Extrapolation Methods in Accelerated Failure Time Models With Covariate Measurement Error.
Jiann-Ping Hsu College of Public Health (JPHCOPH)
It is well known that ignoring measurement errors in covariates in the model leads to biased estimates. Various methods were proposed to address this issue. Simulation and extrapolation (SIMEX) method developed by (He et al. 2007) is a popular method due to its flexibility. It consists of two steps, simulation step and extrapolation step. Although it was investigated for applying to different settings, very little research was done on finding the best extrapolation function.. The objective of this study is to evaluate which extrapolation function gives best estimation in AFT models for data follows Weibull distribution. We use simulation studies to investigate the performance of three most popular extrapolation functions used for the SIMEX method. The results show linear extrapolation function is best for data with small measurement error, quadratic extrapolation function is best for the data with medium measurement error and nonlinear extrapolation function is best for the data with large measurement error. Then we applied these three extrapolation functions to a real data set patients with advanced lung cancer from the North Central Cancer Treatment Group to illustrate the usefulness of the research.
