cervical cancer is the third most common cancer among women in malaysia. the objective of this study was to estimate the effect of explanatory variables on survival time of cervical cancer patients receiving treatment at a hospital in malaysia.in this retrospective record review study, cervical cancer data obtained from hospital universitisains malaysia (husm) was analysed. the data comprises o...

Time-dependent Cox regression and landmarking are the two most commonly used approaches for the analysis of time-dependent covariates in time-to-event data. The estimated effect of the time-dependent covariate in a landmarking analysis is based on the value of the time-dependent covariate at the landmark time point, after which the time-dependent covariate may change value. In this note we deri...

Conclusions In the proportional hazards model the effect of a covariate is assumed to be time-invariant. In this paper a graphical method based on a linear regression model (LRM) is used to test whether this assumption is realistic. The variation in the effect of a covariate is plotted against time. The slope of this plot indicates the nature of the influence of a covariate over time. A covaria...

In this paper we propose an inferential procedure for transformation models with conditional heteroskedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose a consistent estimator and show that it is asymptoticaly √ n normal. We conduct a simulation study that reve...

Mixed and Covariate Dependent Graphical Models by Jie Cheng Co-Chairs: Assoc. Prof. Elizaveta Levina and Prof. Ji Zhu Graphical models have proven to be a useful tool in understanding the conditional dependency structure of multivariate distributions. In Chapters II and III of the thesis, we consider two types of undirected graphical models that are motivated by particular types of applications...

Incorporating covariates into functional principal component analysis (PCA) can substantially improve the representation efficiency of components and predictive performance. However, many existing PCA methods do not make use covariates, those that often have high computational cost or overly simplistic assumptions are violated in practice. In this article, we propose a new framework, called cov...