# Modelling survival data to account for model uncertainty: A single model or model averaging?
> Thamrin S.A.

URL kanonis: https://discover.unhas.ac.id/publications/modelling-survival-data-to-account-for-model-uncertainty-a-single-model-or-model
Jurnal / Konferensi: Springerplus
Tahun terbit: 2013
DOI: https://doi.org/10.1186/2193-1801-2-665
ISSN: 21931801
Citations: 5

## Authors
- Thamrin S.A.

## Abstract
This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single "best" model, where "best" is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as "best", suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival.

## Keywords
- Goodness of fit
- Covariate
- Model selection
- Computer science
- Bayesian probability
- Accelerated failure time model
- Bayesian information criterion
- Sample size determination
- Weibull distribution
- Statistics
- Bayesian inference
- Statistical model
- Data mining
- Econometrics
- Mathematics

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.