Stochastic Ordering under Conditional Modelling of Extreme Values: Drug-Induced Liver Injury

TL;DR

This paper develops a statistical model to better understand and predict severe drug-induced liver injury by analyzing extreme values of liver variables, incorporating dose-dependent survival curve ordering.

Contribution

It extends the Heffernan and Tawn (2004) model by including stochastic ordering of survival curves across different drug doses in clinical studies.

Findings

Enhanced model for extreme liver injury prediction.

Incorporation of dose-dependent survival curve ordering.

Improved early detection of DILI signals.

Abstract

Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver specific variables indicate potential DILI (Hy's Law). We estimate the probability of severe DILI using the Heffernan and Tawn (2004) conditional dependence model which arises naturally in applications where a multidimensional random variable is extreme in at least one component. We extend the current model by including the assumption of stochastically ordered survival curves for different doses in a Phase 3 study.