Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.Comment: Published in at http://dx.doi.org/10.1214/09-STS315 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Incorporating Individual and Collective Ethics into Phase I Cancer Trial Designs

A general framework is proposed for Bayesian model-based designs of Phase I cancer trials, in which a general criterion for coherence (Cheung, 2005) of a design is also developed. This framework can incorporate both "individual" and "collective" ethics into the design of the trial. We propose a new design which minimizes a risk function composed of two terms, with one representing the individual risk of the current dose and the other representing the collective risk. The performance of this design, which is measured in terms of the accuracy of the estimated target dose at the end of the trial, the toxicity and overdose rates, and certain loss functions reflecting the individual and collective ethics, is studied and compared with existing Bayesian model-based designs and is shown to have better performance than existing designs

BaySize: Bayesian Sample Size Planning for Phase I Dose-Finding Trials

We propose BaySize, a sample size calculator for phase I clinical trials using Bayesian models. BaySize applies the concept of effect size in dose finding, assuming the MTD is defined based on an equivalence interval. Leveraging a decision framework that involves composite hypotheses, BaySize utilizes two prior distributions, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under desirable statistical power. Look-up tables are generated to facilitate practical applications. To our knowledge, BaySize is the first sample size tool that can be applied to a broad range of phase I trial designs

Use of Simultaneous Inference Under Order Restriction, Stepdown Testing Procedure and Stage-wise Sequential Optimal Design in Clinical Dose Study

This dissertation discusses the design approaches of adaptive dose escalation study and the analysis methods of dose study data, and the relationship between the study design approach and data analysis methods.A general max-min approach to construct simultaneous confidence Intervals for the monotone means of correlated and normally istributed random samples is proposed to analyze correlated dose response data. The approach provides an accurate, flexible and computationally easy way to obtain critical values of simultaneous confidence intervals under monotone order restriction.Stepdown testing procedure for analyzing dose study data is examined and an modified stepdown testing approach is proposed to incorporate the adaptive sampling nature of the study data. An approximate mixture normal distribution of the dose response is proposed to analyze the binary outcome with small sample size at the first stage of the adaptive design.Finally, an optimal stage-wise adptive clinical dose study design is proposed to be applied in dose escalation study with binary outcome and correlated dose response. The study design criteria is defined as a weighted average power to identify all effective dose levels. A back induction algorithm is used to obtain the design parameters. The values of optimal design parameters vary when different analysis methods are used to analyze the study data.Simulation studies are performed to illustrate the two proposed analysis methods and the proposed optimal design approach

A Hierarchical Bayesian Design for Phase I Trials of Novel Combinations of Cancer Therapeutic Agents

We propose a hierarchical model for the probability of dose-limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78700/1/j.1541-0420.2009.01363.x.pd