Search

Enhanced knowledge of the biological and genetic basis of cancer is re-defining the target population for new treatments. In oncology, potential targets for a new therapeutic agent often include various solid and hematologic malignancies that share common signaling pathways. New agents are often tested in multiple tumor types across which information can be borrowed. We propose a hierarchical Bayesian design (HBD) to simultaneously test a novel agent in multiple groups for randomized Phase II clinical trials with binary endpoints. Compared to parallel design for individual tumor groups, the HBD has greatly reduced sample size. Therefore, this improves efficiency and decreases the financial cost of conducting randomized Phase II clinical trials. An R package hbdct has been developed to implement the HBD and streamline the sample size calibration.

Traditionally, Phase II trials have been conducted as single-arm trials to compare the response probabilities between an experimental therapy and a historical control. Historical control data, however, often have a small sample size, are collected from a different patient population, or use a different response assessment method, so that a direct comparison between a historical control and an experimental therapy may be severely biased. Randomized Phase II trials entering patients prospectively to both experimental and control arms have been proposed to avoid any bias in such cases. The small sample sizes for typical Phase II clinical trials imply that the use of exact statistical methods for their design and analysis is appropriate. In this article, we propose two-stage randomized Phase II trials based on Fisher's exact test, which does not require specification of the response probability of the control arm for testing. Through numerical studies, we observe that the proposed method controls the type I error accurately and maintains a high power. If we specify the response probabilities of the two arms under the alternative hypothesis, we can identify good randomized Phase II trial designs by adopting the Simon's minimax and optimal design concepts that were developed for single-arm Phase II trials.

As cancer treatment development has shifted its attention to targeted therapies, it is becoming increasingly important to provide tools for selecting the right treatment for an individual patient to achieve optimal clinical benefit. Biomarkers, identified and studied in the process of understanding the nature of the disease at the molecular pathogenesis level, have been increasingly recognized as a critical aspect in more accurate diagnosis, prognosis assessment, and therapeutic targeting. Predictive biomarkers, which can aid treatment decisions, require extensive data for validation. In this article, we discuss the definition, clinical usages, and more extensively the clinical trial designs for the validation of predictive biomarkers. Predictive biomarker validation methods can be broadly grouped into retrospective and prospective designs. Retrospective validation utilizes data from previously conducted prospective randomized controlled trials. Prospective designs include enrichment designs, treatment-by-marker interaction designs, marker-based strategy designs, and adaptive designs. We discuss each design with examples and provide comparisons of the advantages and disadvantages among the different designs. We conclude that the combination of scientific, clinical, statistical, ethical, and practical considerations provides guidance for the choice of the clinical trial design for validation of each proposed predictive biomarker.

In this commentary we discuss several challenges that are of current relevance to the design of clinical trials in oncology. We argue that the compartmentalization of trials into the three standard phases, with non overlapping aims, is not necessary and in fact may slow the clinical development of agents. Combined Phase I/II trials and/or Phase I trials that at minimum collect efficacy data and more optimally include a preliminary measure of efficacy in dosing determination should be more widely utilized. Similarly, we posit that randomized Phase II trials should be used more frequently, as opposed to the traditional historical single arm Phase II trial that usually does not have a valid comparison group. The use of non binary endpoints is a simple modification that can improve the efficiency of early phase trials. The heterogeneity in scientific goals and contexts in early phase oncology trials is considerable, and the potential to improve the design to match these goals is great. We review these and other issues in the context of 5 manuscripts related to Phase II trials published in this volume. Our overall premise is that the potential benefits associated with the oncology clinical trial community moving away from the one size fits all paradigm of trial design are great, and that more flexible and efficient designs tailored to match the goals of each study are currently available and being used successfully.

With the growth in knowledge of tumorigenesis, the development of agents to combat cancer has evolved from examining the ability of a compound to destroy more tumor cells than healthy cells without...

We present an approach to smoothing balanced, single–error term analysis of variance (ANOVA), descended from Smith, that also allows spatial, temporal, or spatiotemporal smoothing. The approach addresses unreplicated designs, masked contrasts in effects with many degrees of freedom, and subgroup analysis, demonstrated using a study of denture-lining materials. Our approach is Bayesian but can be viewed as a way to generate frequentist procedures. A simulation experiment compares four priors, unsmoothed ANOVA, and dropping nonsignificant interactions. Three priors have advantages when some interactions are absent; dropping nonsignificant interactions has serious flaws. We contrast our approach with the approaches of Nobile–Green and Gelman.

Although the three-outcome phase II clinical trial design (Sargent et al., 2001) has seen increasing use, confidence interval methodology for the binary endpoint had not been explicitly defined. Typical phase II clinical trial designs with binomial endpoints use a sequential binomial confidence interval (SBCI) algorithm (Duffy and Santner, 1987). We reviewed the SBCI literature and software in order to describe the construction of confidence intervals for the three-outcome setting. Simulations confirmed that proper application of the SBCI algorithm provides appropriate coverage for the three-outcome design.

SummaryWe have produced a reduced linkage map of a diploid Fragaria backcross progeny (F. vesca [F. vesca F. viridis]) denoted 815 x 903BC from 31 microsatellite (SSR) and two gene-specific markers selected from the diploid Fragaria reference map FV FN. The 33 sequence tagged-site (STS) markers selected, segregated in seven linkage groups, and marker order was conserved between FV FN and 815 x 903BC. Significant segregation distortion, observed on three of the seven linkage groups, was accompanied by a reduction in observed recombination frequency and, therefore, genetic distance between markers mapped in these linkage groups. However, this distortion did not affect marker order compared to FV FN and may indicate non-random chromosomal divergence during speciation in Fragaria. Our findings indicate that no major genome reorganisation has occurred during speciation in the genus, and suggest that STS linkage maps of other Fragaria populations, including those of the cultivated strawberry F. ananassa, could ...