The New England Journal of Statistics in Data Science

Cure models are gaining more and more popularity for modeling time-to-event data for different forms of cancer, for which a considerable proportion of patients are considered “cured.” Two types of cure models are widely used, the mixture cure model (MCM) and the promotion time cure model (PTCM). In this article, we propose a unified estimand Δ for comparing treatment and control groups under the survival models with cure fraction, which focuses on whether the treatment extends survival for patients. In addition, we introduce a general framework of Bayesian inference under the cure models. Simulation studies demonstrate that regardless of whether the model is correctly specified, the inference of the unified estimand Δ yields desirable empirical performance. We analyze the ECOG’s melanoma cancer data E1684 via the unified estimand Δ under different models to further demonstrate the proposed methodology.

Basket trials have captured much attention in oncology research in recent years, as advances in health technology have opened up the possibility of classification of patients at the genomic level. Bayesian methods are particularly prevalent in basket trials as the hierarchical structure is adapted to basket trials to allow for information borrowing. In this article, we extend the Bayesian methods to basket trials with treatment and control arms for continuous endpoints, which are often the cases in clinical trials for rare diseases. To account for the imbalance in the covariates which are potentially strong predictors but not stratified in a randomized trial, our models make adjustments for these covariates, and allow different coefficients across baskets. In addition, comparisons are drawn between two-stage design and one-stage design for the four Bayesian methods. Extensive simulation studies are conducted to examine the empirical performance of all models under consideration. A real data analysis is carried out to further demonstrate the usefulness of the Bayesian methods.

We are pleased to launch the first issue of the New England Journal of Statistics in Data Science (NEJSDS). NEJSDS is the official journal of the New England Statistical Society (NESS) under the leadership of Vice President for Journal and Publication and sponsored by the College of Liberal Arts and Sciences, University of Connecticut. The aims of the journal are to serve as an interface between statistics and other disciplines in data science, to encourage researchers to exchange innovative ideas, and to promote data science methods to the general scientific community. The journal publishes high quality original research, novel applications, and timely review articles in all aspects of data science, including all areas of statistical methodology, methods of machine learning, and artificial intelligence, novel algorithms, computational methods, data management and manipulation, applications of data science methods, among others. We encourage authors to submit collaborative work driven by real life problems posed by researchers, administrators, educators, or other stakeholders, and which require original and innovative solutions from data scientists.