Detecting influential data in multivariate survival models
Tsirizani M. Kaombe, Samuel Manda
Abstract
Statistical techniques for detecting influential data are well developed and commonly used in linear regression, and to some extent in linear mixed-effects models. However, even though the application of multivariate survival models is widely undertaken, the development of diagnostic tools for the models has received less attention. In this article, we extend the martingale-based residuals and leverage commonly used in univariate survival regression to derive influence statistics for the multivariate survival model. The performance of the proposed statistic is evaluated by simulation studies. The statistic is illustrated with an analysis of child clustered survival data to identify influential clusters of observations and their effects on the estimate of fixed-effect coefficients.