Assessment of treatment effect heterogeneity is a challenging problem in biostatistics, particularly in clinical trials: Estimation of treatment effects within subgroups in an exploratory setting is often unreliable due to limited sample sizes and multiplicity issues. Through the past decades, many efforts have been made to address this problem. Among them, Muysers et al. (2020) considered...
A question that arises in the analysis of adverse events is how to account for patients who withdraw their consent or switch treatment. One approach is to consider consent withdrawal and treatment switch as competing events. Alternatively, patients who withdraw from the study or switch treatment could be censored, but this implies that one assumes censoring due to treatment switch or consent...
As medicine enters an era of precision, the challenge for statistics is no longer whether personalized care is possible, but how best to translate its potential into clinical practice. Zhao et al. (2012) formulated the personalized medicine problem as finding the optimal individual treatment rule (ITR) by maximizing the expected clinical responses. More recently, Lei and Candès (2021)...
Continuous monitoring (CM) at AstraZeneca is the systematic review and evaluation of accumulating study data to inform timely decisions. Rather than waiting for formal interims or study completion, our CM approach in early phase oncology studies, enables earlier data-driven decisions to stop for futility or safety, minimising exposure to ineffective or unsafe treatments, and to accelerate...
Continuous monitoring is becoming more popular due to its significant benefits, including reducing sample sizes and reaching earlier conclusions. In general, it involves monitoring nuisance parameters (e.g., the variance of outcomes) until a specific condition is satisfied. The blinded method, which does not require revealing group assignments, was recommended because it maintains the...
