Statistical Bioinformatics Seminar: Dr Dr Aishwarya Bhaskaran

Hosted by Sydney Precision Data Science Centre 

Speaker: Dr Aishwarya Bhaskaran (Macquarie University) 

Abstract: Accelerated failure time (AFT) models are frequently used for modelling
survival data.  This approach is attractive as it quantifies the direct relationship
between the time until an event occurs and various covariates.  It asserts that the
failure times experience either acceleration or deceleration through a multiplicative
factor when these covariates are present.  While existing literature provides numerous
methods for fitting AFT models with time-fixed covariates, adapting these approaches to
scenarios involving both time-varying covariates and partly interval-censored data
remains challenging.  In this paper, we introduce a maximum penalized likelihood
approach to fit a semiparametric AFT model.  This method, designed for survival data
with partly interval-censored failure times, accommodates both time-fixed and
time-varying covariates.  We utilize Gaussian basis functions to construct a smooth
approximation of the nonparametric baseline hazard and fit the model via a constrained
optimization approach.  To illustrate the effectiveness of our proposed method, we
conducted a comprehensive simulation study.  We also present an implementation of our
approach on a randomized clinical trial dataset on advanced melanoma patients.  

About the speaker: Dr Aishwarya Bhaskaran earned her PhD from the University of
Technology Sydney, where she was supervised by Professor Matt Wand.  Her PhD research
focused on likelihood theory and methods for generalized linear mixed models.
Currently, she is a postdoctoral research fellow at Macquarie University, where she
specializes in analyzing high-performance predictive models using semi-parametric
survival regression.  

This event will be held in-person and online.  

Venue: Access Grid Room, Level 8, Carslaw Building 

Zoom: https://uni-sydney.zoom.us/j/84087321707