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Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected type I error, as well as the possibility of a costlier and lengthier trial. This motivates an investigation of the feasibility of hierarchical Bayesian methods for incorporating historical data that are adaptively robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this article, we present several models that allow for the commensurability of the information in the historical and current data to determine how much historical information is used. A primary tool is elaborating the traditional power prior approach based upon a measure of commensurability for Gaussian data. We compare the frequentist performance of several methods using simulations, and close with an example of a colon cancer trial that illustrates a linear models extension of our adaptive borrowing approach. Our proposed methods produce more precise estimates of the model parameters, in particular, conferring statistical significance to the observed reduction in tumor size for the experimental regimen as compared to the control regimen.

Midostaurin, an oral multitargeted kinase inhibitor, is active in patients with a FLT3 mutation. Among patients with acute myeloid leukemia and this mutation, the addition of midostaurin to standard chemotherapy appeared to improve long-term outcomes.

Tumours respond differently to immunotherapies compared with chemotherapeutic drugs, raising questions about the assessment of changes in tumour burden—a mainstay of evaluation of cancer therapeutics that provides key information about objective response and disease progression. A consensus guideline—iRECIST—was developed by the RECIST working group for the use of modified Response Evaluation Criteria in Solid Tumours (RECIST version 1.1) in cancer immunotherapy trials, to ensure consistent design and data collection, facilitate the ongoing collection of trial data, and ultimate validation of the guideline. This guideline describes a standard approach to solid tumour measurements and definitions for objective change in tumour size for use in trials in which an immunotherapy is used. Additionally, it defines the minimum datapoints required from future trials and those currently in development to facilitate the compilation of a data warehouse to use to later validate iRECIST. An unprecedented number of trials have been done, initiated, or are planned to test new immune modulators for cancer therapy using a variety of modified response criteria. This guideline will allow consistent conduct, interpretation, and analysis of trials of immunotherapies.

The landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model-based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades, but they are difficult to implement because of the use of complicated dose–toxicity models and the requirement to refit the model at each decision of dose escalation and de-escalation. We propose a generalized Bayesian optimal interval design, gBOIN, that accommodates various existing toxicity grade scoring systems under a unified framework. As a model-assisted design, gBOIN derives its optimal decision rule on the basis of the exponential family of distributions but is carried out in a simple way as the algorithm-based design: its decision of dose escalation and de-escalation involves only a simple comparison of the sample mean of the end point with two prespecified dose escalation and deescalation boundaries. No model fitting is needed. We show that gBOIN has the desirable finite property of coherence and a large sample property of consistency. Numerical studies show that gBOIN yields good performance that is comparable with or superior to that of some existing, more complicated model-based designs. A Web application for implementing gBOIN is freely available from http://www.trialdesign.org.

To determine clinical features, natural history, and outcome of a well-defined cohort of 25 consecutive patients with idiopathic systemic capillary leak syndrome (SCLS) evaluated at a tertiary care center. Records of patients diagnosed as having SCLS from November 1, 1981, through April 30, 2008, were reviewed. Descriptive statistics were used to analyze patient demographics, clinical features, complications, and therapeutic interventions. Of the 34 patients whose records were reviewed, 25 fulfilled all diagnostic criteria for SCLS. The median age at diagnosis of SCLS was 44 years. Median follow-up of surviving patients was 4.9 years, and median time to diagnosis from symptom onset was 1.1 years (interquartile range, 0.5-4.1 years). Flulike illness or myalgia was reported by 14 patients (56%) at onset of an acute attack of SCLS, and rhabdomyolysis developed in 9 patients (36%). Patients with a greater decrease in albumin level had a higher likelihood of developing rhabdomyolysis ( p=.03). Monoclonal gammopathy, predominantly of the IgG-κ type, was found in 19 patients (76%). The progression rate to multiple myeloma was 0.7% per person-year of follow-up. The overall response rate to the different therapies was 76%, and 24% of patients sustained durable (>2 years) complete remission. The estimated 5-year overall survival rate was 76% (95% confidence interval, 59%-97%). Systemic capillary leak syndrome, a rare disease that occurs in those of middle age, is usually diagnosed after a considerable delay from onset of symptoms. The degree of albumin decrement during an attack correlates with development of rhabdomyolysis. A reduction in the frequency and/or the severity of attacks was seen in nearly three-fourths of patients who were offered empirical therapies. The rate of progression to multiple myeloma appears to be comparable to that of monoclonal gammopathy of undetermined significance.

Traditional dose-finding designs are substantially inefficient for targeted agents and cancer immunotherapies by failing to incorporate efficacy signals, mild and moderate adverse events, and late, cumulative toxicities. However, the lack of user-friendly software is a barrier to the practical use of the novel phase I designs, despite their demonstrated superiority of traditional 3+3 designs. To overcome these barriers, we present an R package, phase1RMD, which provides a comprehensive implementation of novel designs with repeated toxicity measures and early efficacy. A novel phase I repeated measures design that used a continuous toxicity score from multiple treatment cycles was implemented. Furthermore, in studies where preliminary efficacy is evaluated, an adaptive, multi-stage design to identify the most efficacious dose with acceptable toxicity was demonstrated. Functions are provided to recommend the next dose based on the data collected in a phase I trial, as well as to assess trial characteristics given design parameters via simulations. The repeated measure designs accurately estimated both the magnitude and direction of toxicity trends in late treatment cycles, and allocated more patients at therapeutic doses. The R package for implementing these designs is available from the Comprehensive R Archive Network. To our best knowledge, this is the first software that implement novel phase I dose-finding designs that simultaneously accounts for the multiple-grade toxicity events over multiple treatment cycles and a continuous early efficacy outcome. With the software published on CRAN, we will pursue the implementation of these designs in phase I trials in real-life settings.

The COVID-19 pandemic has had widespread impact on healthcare, resulting in modifications to how we perform cancer research, including clinical trials for cancer. The impact of some healthcare workers and study coordinators working remotely and patients minimizing visits to medical facilities impacted clinical trial participation. Clinical trial accrual dropped at the onset of the pandemic, with improvement over time. Adjustments were made to some trial protocols, allowing telephone or video-enabled consent. Certain study activities were permitted to be performed by local healthcare providers or at local laboratories to maximize patients’ ability to continue on study during these challenging times. We discuss the impact of COVID-19 on cancer clinical trials and changes at the local, cooperative group, and national level.

Background Eligibility criteria and endpoints for cancer cachexia trials—and whether weight loss should be included—remain controversial. Although most cachexia trials enrol patients after initial cancer diagnosis, few studies have addressed whether weight loss well after a cancer diagnosis is prognostic. Methods We pooled data from non‐small cell lung cancer patients from prospectively conducted trials within the Alliance for Clinical Trials in Oncology (1998–2008), a nationally funded infrastructure. We examined (i) weight data availability and weight changes and (ii) survival. Results A total of 822 patients were examined. Of these, 659 (80%) were on treatment at the beginning of Cycle 2 of chemotherapy; weight was available for 656 (80%). By Cycles 3 and 4, weight was available for 448 (55%) and 384 (47%), respectively. From baseline to immediately prior to Cycle 2, 208 (32%) gained weight; 225 (34%) lost <2% of baseline weight; and 223 (34% of 656) lost 2% or more. Median survival from the beginning of Cycle 2 was 13.0, 10.9, and 6.9 months for patients with weight gain, weight loss of <2%, and weight loss of 2% or more, respectively. In multivariate analyses, adjusted for age, sex, performance score, type of treatment, and body mass index, weight loss of 2% or more was associated with poor overall survival compared with weight gain [hazard ratio (HR) = 1.66; 95% confidence interval (CI): 1.33–2.07; P < 0.001] and compared with weight loss of <2% (HR = 1.57; 95% CI: 1.27–1.95; P < 0.001). Although weight loss of <2% was not associated with poorer overall survival compared with weight gain, it was associated with poorer progression‐free survival (HR = 1.24; 95% CI: 1.01–1.51; P = 0.036). Similar findings were observed in a separate 255‐patient validation cohort. Conclusions Weight should be integrated into cancer cachexia trials because of its ease of frequent measurement and sustained prognostic association.

This article introduces the adverse event (AE) burden score. The AE burden by treatment cycle is a weighted sum of all grades and AEs that the patient experienced in a cycle. The overall AE burden score is the total AE burden the patient experienced across all treatment cycles. AE data from two completed Alliance multi-center randomized double-blind placebo-controlled trials, with different AE profiles (NCCTG 97-24-51: 176 patients, and A091105: 83 patients), were utilized for illustration. Results of the AE burden score analyses corroborated the trials’ primary results. In 97-24-51, the overall AE burden for patients on the treatment arm was 2.2 points higher than those on the placebo arm, with a higher AE burden for patients who went off treatment early due to AE. Similarly, in A091105, the overall AE burden was 1.6 points higher on the treatment arm. On the placebo arms, the AE burden in 97-24-51 remained constant over time; and increased in later cycles in A091105, likely attributable to the increase in disease morbidity. The AE burden score enables statistical comparisons analogous to other quantitative endpoints in clinical trials, and can readily accommodate different trial settings, diseases, and treatments, with diverse AE profiles.