We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients be initially randomized among four chemotherapies, and that those who responded poorly be re-randomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs’ overall success rates, with success defined by the occurrence of successful responses in each of two consecutive courses of the patient’s therapy. Of the 150 study participants, 47 did not complete their therapy as per the algorithm. However, 35 of them did so for reasons that precluded further chemotherapy, that is, toxicity and/or progressive disease. Consequently, rather than comparing the overall success rates of the DTRs in the unrealistic event that these patients had remained on their assigned chemotherapies, we conducted an analysis that compared viable switch rules defined by the per-protocol rules but wi...

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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer.

Individualized medical decision making is often complex due to patient treatment response heterogeneity. Pharmacotherapy may exhibit distinct efficacy and safety profiles for different patient popu...

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Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: With an Application to Treating Type 2 Diabetes Patients With Insulin Therapies

The emergence of molecularly targeted agents in oncology has not only revolutionized the care of cancer patients, but also changed the daily practice of medical oncologists. Molecularly targeted agents indeed often differ from traditional cytotoxic agents by their administration schedules and routes, their toxicity profiles, and/or the assessment of their antitumor activity. In addition, the observation that molecularly targeted agents sometimes have limited antitumor activity as single agents has led clinical investigators to combine molecularly targeted agents together or with cytotoxic agents. We review here the current challenges for the early clinical development of anticancer agents in the era of molecularly targeted agents. We focus on the choice of end points in phase I oncology clinical trials, as well as on the choice of dose escalation methods with an emphasis on available dose escalation methods for molecularly targeted agents and for combination trials.

Current challenges for the early clinical development of anticancer drugs in the era of molecularly targeted agents

A sequentially outcome-adaptive Bayesian design is proposed for choosing the dose of an experimental therapy based on elicited utilities of a bivariate ordinal (toxicity, efficacy) outcome. Subject to posterior acceptability criteria to control the risk of severe toxicity and exclude unpromising doses, patients are randomized adaptively among the doses having posterior mean utilities near the maximum. The utility increment used to define near-optimality is nonincreasing with sample size. The adaptive randomization uses each dose's posterior probability of a set of good outcomes, defined by a lower utility cutoff. Saturated parametric models are assumed for the marginal dose-toxicity and dose-efficacy distributions, allowing the possible requirement of monotonicity in dose, and a copula is used to obtain a joint distribution. Prior means are computed by simulation using elicited outcome probabilities, and prior variances are calibrated to control prior effective sample size and obtain a design with good operating characteristics. The method is illustrated by a Phase I/II trial of radiation therapy for children with brainstem gliomas.

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Adaptive randomization to improve utility-based dose-finding with bivariate ordinal outcomes.

Anticancer agents used in combination are fundamental to successful cancer treatment, particularly in a curative setting. For dual-agent phase I trials, the goal is to identify drug doses and schedules for further clinical testing. However, current methods for establishing the recommended phase II dose for agents in combination can fail to fully explore drug interactions. With increasing numbers of anticancer drugs requiring testing, new adaptive model-based trial designs that improve on current practice have been proposed, although uptake has been minimal. We describe the methods available and discuss some of the opportunities and challenges faced in dual-agent phase I trials, as well as giving examples of trials in which adaptive designs have been implemented successfully. Improving the design and execution of phase I trials of drug combinations critically relies on collaboration between the statistical and clinical communities to facilitate the implementation of adaptive, model-based designs.

Adaptive designs for dual-agent phase I dose-escalation studies

An outcome-adaptive Bayesian design is proposed for choosing the optimal dose pair of a chemotherapeutic agent and a biological agent used in combination in a phase I/II clinical trial. Patient outcome is characterized as a vector of two ordinal variables accounting for toxicity and treatment efficacy. A generalization of the Aranda-Ordaz model (1981, Biometrika 68, 357-363) is used for the marginal outcome probabilities as functions of a dose pair, and a Gaussian copula is assumed to obtain joint distributions. Numerical utilities of all elementary patient outcomes, allowing the possibility that efficacy is inevaluable due to severe toxicity, are obtained using an elicitation method aimed to establish consensus among the physicians planning the trial. For each successive patient cohort, a dose pair is chosen to maximize the posterior mean utility. The method is illustrated by a trial in bladder cancer, including simulation studies of the method's sensitivity to prior parameters, the numerical utilities, correlation between the outcomes, sample size, cohort size, and starting dose pair.

https://odin.mdacc.tmc.edu/~pfthall/main/Biometrics_2dose_utility.pdf

Andrew Kramar

Papers

Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials