Comparison of variance estimation methods in semiparametric accelerated failure time models for multivariate failure time data. (English) Zbl 1478.62302
Summary: The semiparametric accelerated failure time (AFT) model is a log-linear model of failure times with an unspecified random error term. The rank-based estimator has been a popular estimation method for regression parameters in this model. An induced smoothing method has reduced computational complexity and instability in the original non-smooth rank-based estimator. This paper briefly reviews and compares the recently proposed computationally efficient variance estimation methods for the semiparametric AFT models in multivariate failure times settings. Comparisons are made via extensive simulation experiments. Based on our findings, we may recommend using ‘Diff-Boot’ and ‘Diff-Closed’ methods with a one-step iteration. These variance estimators are then illustrated with the well-known Diabetic retinopathy study data.
MSC:
| 62N05 | Reliability and life testing |
| 62G05 | Nonparametric estimation |
| 62H12 | Estimation in multivariate analysis |
| 62N02 | Estimation in survival analysis and censored data |
| 62-08 | Computational methods for problems pertaining to statistics |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
induced smoothing; iterative methods; multiplier bootstrap; rank-based estimation; resampling; sandwich variance estimatorReferences:
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