A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials.

In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint.

Covariate-adjusted value-guided subgroup identification via boosting.

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification.

An extension to a 2-in-1 adaptive design with biomarker subpopulation selection.

In a recent article, Jin and Zhang (2022) proposed an adaptive 2-in-1 design which can expand an ongoing Phase 2 trial with multiple treatment or dose arms into a confirmatory Phase 3 trial with the selected arms based on interim data, and proved that the design can preserve the familywise Type I error rate under a mild assumption. The proposed adaptive design provides an efficient pathway to combine the treatment or dose selection stage and the confirmatory stage into one trial and can expedite drug development. Here we extend the adaptive 2-in-1 design by Jin and Zhang (2022) to an adaptive 2-in-1 design with biomarker subpopulation selection with a similar framework.

An Extended Framework of Multiple Testing in Group Sequential Design.

Here, we consider testing multiple hypotheses in group sequential trials. A graphical multiple test procedure was proposed for group sequential trials using weighted Bonferroni test. In this paper, we extend the framework for the graph-based group sequential procedure by applying a modified weighted Simes test.

A variation of a 2-in-1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development.

In a recent article, Zhang et al. proposed a 2-in-1 adaptive design to seamlessly expand a selected dose, based on efficacy compared to the control arm, from a Phase 2 trial to a Phase 3 trial for oncology drug development. In this article, we communicate a variation of the proposed design which selects a dose to expand based on direct comparison of high dose to low dose when both doses demonstrate promising efficacy compared to the control arm.

A 2-in-1 adaptive design to seamlessly expand a selected dose from a phase 2 trial to a phase 3 trial for oncology drug development.

In oncology, dose-finding studies are largely performed only in Phase I clinical trials and the maximum tolerated dose (MTD), a dose initially developed for systemic chemotherapies, is by default selected for the Phase 3 confirmatory trial. With the advent of anti-cancer therapies such as molecular targeted agents and immunotherapies, a paradigm shift is underway from the use of conventional MTD approaches to improved dose selection strategies for oncology programs. In response to this new challenge, new study designs are needed to optimize dose selection while still bring life-changing new therapies to patients as soon as possible.

Health-Related Quality of Life of Patients with Recurrent or Metastatic Cutaneous Squamous Cell Carcinoma Treated with Pembrolizumab in KEYNOTE-629.

Introduction: Pembrolizumab provided durable responses and acceptable safety in recurrent or metastatic (R/M) cutaneous squamous cell carcinoma (cSCC) in the KEYNOTE-629 study. In this elderly, fragile population with disfiguring tumours, preservation of health-related quality of life (HRQoL) is critical. Here, we present pre-specified exploratory HRQoL analyses from the first interim analysis of KEYNOTE-629.

Adjuvant Pembrolizumab after Nephrectomy in Renal-Cell Carcinoma.

Background: Patients with renal-cell carcinoma who undergo nephrectomy have no options for adjuvant therapy to reduce the risk of recurrence that have high levels of supporting evidence.

Methods: In a double-blind, phase 3 trial, we randomly assigned, in a 1:1 ratio, patients with clear-cell renal-cell carcinoma who were at high risk for recurrence after nephrectomy, with or without metastasectomy, to receive either adjuvant pembrolizumab (at a dose of 200 mg) or placebo intravenously once every 3 weeks for up to 17 cycles (approximately 1 year). The primary end point was disease-free survival according to the investigator's assessment.

Artificial Intelligence Can Effectively Predict Early Hematoma Expansion of Intracerebral Hemorrhage Analyzing Noncontrast Computed Tomography Image.

This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction.

An adaptive seamless Phase 2-3 design with multiple endpoints.

Adaptive seamless Phase 2-3 design has been considered as one possible way to expedite development time for a drug program by allowing the expansion from an ongoing Phase 2 trial into a Phase 3 trial. Multiple endpoints are often tested when a regulatory approval is pursued. Here we propose an adaptive seamless Phase 2-3 design with multiple endpoints which can expand an ongoing Phase 2 trial into a Phase 3 trial based on an intermediate endpoint for adaptive decision and test the endpoints with a powerful multiple test procedure.

A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data.

In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function.

Pembrolizumab Monotherapy for Recurrent or Metastatic Cutaneous Squamous Cell Carcinoma: A Single-Arm Phase II Trial (KEYNOTE-629).

Purpose: Treatment options are limited for patients with recurrent and/or metastatic (R/M) cutaneous squamous cell carcinoma (cSCC); mortality rates exceed 70% in patients with distant metastases. Here, we present the first interim analysis of the R/M cSCC cohort from the 2-cohort-locally advanced and R/M-phase II KEYNOTE-629 study.

Patients And Methods: Patients with R/M cSCC not amenable to surgery or radiation received pembrolizumab 200 mg every 3 weeks.

Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study.

A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis.

Finding novel genes by testing G × E interactions in a genome-wide association study.

In a genome-wide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each single nucleotide polymorphism (SNP) with the trait. Some SNPs that are truly associated with the trait may not be identified in this scan if they have a weak marginal effect and thus low power to be detected. However, these SNPs may be quite important in subgroups of the population defined by an environmental or personal factor, and may be detectable if such a factor is carefully considered in a gene-environment (G × E) interaction analysis.