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Diagnostics for a two-stage joint survival model.
- Source :
-
Communications in Statistics: Simulation & Computation . 2023, Vol. 52 Issue 11, p5163-5177. 15p. - Publication Year :
- 2023
-
Abstract
- A two-stage joint survival model is used to analyze time to event outcomes that could be associated with biomakers that are repeatedly collected over time. A Two-stage joint survival model has limited model checking tools and is usually assessed using standard diagnostic tools for survival models. The diagnostic tools can be improved and implemented. Time-varying covariates in a two-stage joint survival model might contain outlying observations or subjects. In this study we used the variance shift outlier model (VSOM) to detect and down-weight outliers in the first stage of the two-stage joint survival model. This entails fitting a VSOM at the observation level and a VSOM at the subject level, and then fitting a combined VSOM for the identified outliers. The fitted values were then extracted from the combined VSOM which were then used as time-varying covariate in the extended Cox model. We illustrate this methodology on a dataset from a multi-center randomized clinical trial. A multi-center trial showed that a combined VSOM fits the data better than an extended Cox model. We noted that implementing a combined VSOM, when desired, has a better fit based on the fact that outliers are down-weighted. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SURVIVAL analysis (Biometry)
*CLINICAL trials
Subjects
Details
- Language :
- English
- ISSN :
- 03610918
- Volume :
- 52
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Simulation & Computation
- Publication Type :
- Academic Journal
- Accession number :
- 173686784
- Full Text :
- https://doi.org/10.1080/03610918.2021.1995751