Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach
Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regressio...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tehran University of Medical Sciences
2023-06-01
|
| Series: | Journal of Biostatistics and Epidemiology |
| Subjects: | |
| Online Access: | https://jbe.tums.ac.ir/index.php/jbe/article/view/1271 |
| _version_ | 1797303969562755072 |
|---|---|
| author | Fereshteh Mokhtarpour Mostafa Hosseini Akram Yazdani Mehdi Yaseri |
| author_facet | Fereshteh Mokhtarpour Mostafa Hosseini Akram Yazdani Mehdi Yaseri |
| author_sort | Fereshteh Mokhtarpour |
| collection | DOAJ |
| description |
Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with
Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation.
Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation.
Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.
|
| first_indexed | 2024-03-08T00:02:34Z |
| format | Article |
| id | doaj.art-f647b05be9854efcac90588861dffe47 |
| institution | Directory Open Access Journal |
| issn | 2383-4196 2383-420X |
| language | English |
| last_indexed | 2024-03-08T00:02:34Z |
| publishDate | 2023-06-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Journal of Biostatistics and Epidemiology |
| spelling | doaj.art-f647b05be9854efcac90588861dffe472024-02-18T04:06:24ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2023-06-019210.18502/jbe.v9i2.14629Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance ApproachFereshteh Mokhtarpour0Mostafa Hosseini1Akram Yazdani2Mehdi Yaseri3Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Biostatistics and Epidemiology, Faculty of Health, Kashan University of Medical Sciences, Kashan, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation. Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation. Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex. https://jbe.tums.ac.ir/index.php/jbe/article/view/1271Quantile RegressionMinimum distance approachSurvivalCheck-based modelingInverse cumulative distribution function |
| spellingShingle | Fereshteh Mokhtarpour Mostafa Hosseini Akram Yazdani Mehdi Yaseri Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach Journal of Biostatistics and Epidemiology Quantile Regression Minimum distance approach Survival Check-based modeling Inverse cumulative distribution function |
| title | Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach |
| title_full | Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach |
| title_fullStr | Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach |
| title_full_unstemmed | Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach |
| title_short | Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach |
| title_sort | quantile regression in survival analysis comparing check based modeling and the minimum distance approach |
| topic | Quantile Regression Minimum distance approach Survival Check-based modeling Inverse cumulative distribution function |
| url | https://jbe.tums.ac.ir/index.php/jbe/article/view/1271 |
| work_keys_str_mv | AT fereshtehmokhtarpour quantileregressioninsurvivalanalysiscomparingcheckbasedmodelingandtheminimumdistanceapproach AT mostafahosseini quantileregressioninsurvivalanalysiscomparingcheckbasedmodelingandtheminimumdistanceapproach AT akramyazdani quantileregressioninsurvivalanalysiscomparingcheckbasedmodelingandtheminimumdistanceapproach AT mehdiyaseri quantileregressioninsurvivalanalysiscomparingcheckbasedmodelingandtheminimumdistanceapproach |