Bayesian Uncertainty Directed Trial Designs

Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.The work of SB was partially supported by funding from the Cantab Capital Institute for the Mathematics of Information. LT has been supported by a Burroughs Wellcome Fund Award for Innovation in Regulatory Science and the The Claudia Adams Barr Program in Innovative Basic Cancer Research

To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework.

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on-going clinical trials using a "platform" trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two-stage trial setting, we propose a decision-theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis

TBCRC 048: Phase II Study of Olaparib for Metastatic Breast Cancer and Mutations in Homologous Recombination-Related Genes

PURPOSE Olaparib, a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi), is approved for the treatment of human epidermal growth factor receptor 2 (HER2)–negative metastatic breast cancer (MBC) in germline (g)BRCA1/2 mutation carriers. Olaparib Expanded, an investigator-initiated, phase II study, assessed olaparib response in patients with MBC with somatic (s)BRCA1/2 mutations or g/s mutations in homologous recombination (HR)–related genes other than BRCA1/2. METHODS Eligible patients had MBC with measurable disease and germline mutations in non-BRCA1/2 HR-related genes (cohort 1) or somatic mutations in these genes or BRCA1/2 (cohort 2). Prior PARPi, platinum-refractory disease, or progression on more than two chemotherapy regimens (metastatic setting) was not allowed. Patients received olaparib 300 mg orally twice a day until progression. A single-arm, two-stage design was used. The primary endpoint was objective response rate (ORR); the null hypothesis (# 5% ORR) would be rejected within each cohort if there were four or more responses in 27 patients. Secondary endpoints included clinical benefit rate and progression-free survival (PFS). RESULTS Fifty-four patients enrolled. Seventy-six percent had estrogen receptor–positive HER2-negative disease. Eighty-seven percent had mutations in PALB2, sBRCA1/2, ATM, or CHEK2. In cohort 1, ORR was 33% (90% CI, 19% to 51%) and in cohort 2, 31% (90% CI, 15% to 49%). Confirmed responses were seen only with gPALB2 (ORR, 82%) and sBRCA1/2 (ORR, 50%) mutations. Median PFS was 13.3 months (90% CI, 12 months to not available/computable [NA]) for gPALB2 and 6.3 months (90% CI, 4.4 months to NA) for sBRCA1/ 2 mutation carriers. No responses were observed with ATM or CHEK2 mutations alone. CONCLUSION PARP inhibition is an effective treatment for patients with MBC and gPALB2 or sBRCA1/2 mutations, significantly expanding the population of patients with breast cancer likely to benefit from PARPi beyond gBRCA1/2 mutation carriers. These results emphasize the value of molecular characterization for treatment decisions in MBC

Bayesian uncertainty-directed dose finding designs

We introduce Bayesian uncertainty-directed (BUD) designs for phase I–II dose finding trials. This class of designs assigns patients to candidate dose levels with the aim of maximizing explicit information metrics at completion of the trial, while avoiding the treatment of patients with toxic or ineffective dose levels during the trial. Explicit information metrics provide, at completion of the clinical study, accuracy measures of the final selection of optimal or nearly optimal dose levels. The BUD approach utilizes the decision theoretic framework and builds on utility functions that rank candidate dose levels. The utility of a dose combines the probabilities of toxicity events and the probability of a positive response to treatment. We discuss the application of BUD designs in two distinct settings; dose finding studies for single agents and precision medicine studies with biomarker measurements that allow dose optimization at the individual level. The approach proposed and the simulation scenarios used in the evaluation of BUD designs are motivated by a stereotactic body radiation therapy study in lung cancer at our institution