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Background: A summary measure that reflects the global toxicity burden of a treatment is essential for comparing therapies. Current toxicity summaries are ad hoc and do not distinguish among the severities and types of toxicities. Here a clinically feasible method for estimating the toxicity burden, based on a prospective evaluation of the toxicity profile of a randomized clinical trial of 746 prostate cancer patients conducted by SWOG, is proposed., Methods: For 308 patients who experienced severe toxicities, 2 physicians randomly selected from 14 physicians evaluated each toxicity profile and assigned a visual analogue scale score (0-10) based on their impression of the global burden of toxicities. With mixed-effects models, severity scores and a 10-point toxicity burden score (TBS) were derived from 27 predictors accounting for severe (grade 3) and life-threatening (grade 4) toxicities for each organ class of the Common Terminology Criteria for Adverse Events., Results: For most organ classes, grade 3 toxicities had a TBS of 4.14 (95% confidence interval [CI], 3.65-4.63), but infections, cardiovascular events, and pulmonary events had a higher TBS with differences of 0.87 (95% CI, 0.53-1.21), 0.88 (95% CI, 0.51-1.25), and 0.73 (95% CI, 0.22-1.24), respectively. Moreover, most grade 4 events had a higher TBS than grade 3 events, except for hemorrhaging, pain, metabolic events, and musculoskeletal events. The intrarater and interrater correlations were 0.91 and 0.59, respectively., Conclusions: The burden of toxicity grades differs with toxicity types. A TBS provides a toxicity burden summary that incorporates physicians' perspectives and differentiates between severe and life-threatening toxicities and organ classes. Cancer 2018;124:858-64. © 2017 American Cancer Society., (© 2017 American Cancer Society.)

In oncology, combinations of drugs are often used to improve treatment efficacy and/or reduce harmful side effects. Dual-agent phase I clinical trials assess drug safety and aim to discover a maximum tolerated dose combination via dose-escalation; cohorts of patients are given set doses of both drugs and monitored to see if toxic reactions occur. Dose-escalation decisions for subsequent cohorts are based on the number and severity of observed toxic reactions, and an escalation rule. In a combination trial, drugs may be administered concurrently or non-concurrently over a treatment cycle. For two drugs given non-concurrently with overlapping toxicities, toxicities occurring after administration of the first drug yet before administration of the second may be attributed directly to the first drug, whereas toxicities occurring after both drugs have been given some present ambiguity; toxicities may be attributable to the first drug only, the second drug only or the synergistic combination of both. We call this mixture of attributable and non-attributable toxicity semi-attributable toxicity. Most published methods assume drugs are given concurrently, which may not be reflective of trials with non-concurrent drug administration. We incorporate semi-attributable toxicity into Bayesian modelling for dual-agent phase I trials with non-concurrent drug administration and compare the operating characteristics to an approach where this detail is not considered. Simulations based on a trial for non-concurrent administration of intravesical Cabazitaxel and Cisplatin in early-stage bladder cancer patients are presented for several scenarios and show that including semi-attributable toxicity data reduces the number of patients given overly toxic combinations. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

In comparative clinical trials, the randomization probabilities may be unbalanced adaptively by utilizing the interim data available at each patient's entry time to favour the treatment or treatments having comparatively superior outcomes. This is ethically appealing because, on average, more patients are assigned to the more successful treatments. Consequently, physicians are more likely to enroll patients onto trials where the randomization is outcome-adaptive rather than balanced in the conventional manner. Outcome-adaptive methods based on a binary variable may be applied by reducing an event time to the indicator of the event's occurrence within a predetermined time interval. This results in a loss of information, however, since it ignores the censoring times of patients who have not experienced the event but whose evaluation interval is not complete. This paper proposes and compares exact and approximate Bayesian outcome-adaptive randomization procedures based on time-to-event outcomes. The procedures account for baseline prognostic covariates, and they may be applied continuously over the course of the trial. We illustrate these methods by application to a phase II selection trial in acute leukaemia. A simulation study in the context of this trial is presented., (Copyright 2005 John Wiley & Sons, Ltd.)