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

Designing Theory-based Curriculum to Positively Affect Psychosocial Variables for Weight Loss in College Students

Education and Human Ecology: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)The percent of overweight/obese college students increased from 20.5% to 33.5% between 1995 and 2010. This growing segment of our college population is both understudied and underserved. The purpose of this multi-phase study was to investigate the feasibility of an academic class to promote exercise and weight loss by targeting theory-based psychosocial variables. One section of a Conditioning Principles academic class was re-designed to positively change psychosocial variables related to exercise, while maintaining required class content. The teaching strategies were based on assumptions from Self-Determination Theory (SDT) and focused on increasing intrinsic motivation and improving self-efficacy for exercise. Students self-selected into the intervention section or one of two standard sections. Comparison classes were used to evaluate the effects of the intervention on proposed mediators of exercise behavior beyond changes achieved through the existing curriculum. Students completed questionnaires to assess motivational regulations, self-efficacies, eating behaviors, social support for exercise, body image, and physical activity during the first and last weeks of a 10-week academic term. Height and weight were also used to examine possible moderating effects of BMI. This paper presents the study design, procedures, and a detailed intervention plan, including teaching strategies and associated target variables. If results indicate the revised class positively influenced exercise mediators, supplementing standard curriculum with SDT strategies could improve college student health and fitness. Further, insight into the moderating effect of BMI status on the mediating variables would enable tailoring of academic classes to the needs of overweight/obese students.No embarg

Students with Disabilities: Transitioning from PK-18 to the Workplace

The case study investigates the conflict that arises when a student who has received extensive assistance and accommodation for an invisible disability throughout her education (PK-12 through graduate school) transitions to her first job. The case explores the tension between the employee and her employer

Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via Copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations

Simultaneously Optimizing Dose and Schedule of a New Cytotoxic Agent

Traditionally, phase I clinical trial designs determine a maximum tolerated dose of an experimental cytotoxic agent based on a fixed schedule, usually one course consisting of multiple administrations, while varying the dose per administration between patients. However, in actual medical practice patients often receive several courses of treatment, and some patients may receive one or more dose reductions due to low-grade (non-dose limiting) toxicity in previous courses. As a result, the overall risk of toxicity for each patient is a function of both the schedule and the dose used at each adminstration. We propose a new paradigm for Phase I clinical trials that allows both the dose per administration and the schedule to vary, making treatment two-dimensional. We provide an outcome-adaptive Bayesian design that simultaneously optimizes both dose and schedule in terms of the overall risk of toxicity, based on time-to-toxicity outcomes. The method is illustrated with a trial of an agent hypothesized to prolong cancer remission after allogeneic bone marrow transplantation, and a simulation study in the context of this trial is presented

ALICE: Study of Financial Hardship-Louisiana

Through a series of new, standardized measurements, the United Way ALICE Reports present a broad picture of financial insecurity at the county and town level, and the reasons for why. What we found was startling -- the size of the workforce in each state that is struggling financially is much higher than traditional federal poverty guidelines suggest. The United Way ALICE Project is a grassroots movement stimulating a fresh, nonpartisan national dialogue about how to reverse the trend and improve conditions for this growing population of families living paycheck to paycheck

ALICE: Study of Financial Hardship-Pacific Northwest: Idaho, Oregon and Washington

Through a series of new, standardized measurements, the United Way ALICE Reports present a broad picture of financial insecurity at the county and town level, and the reasons for why. What we found was startling -- the size of the workforce in each state that is struggling financially is much higher than traditional federal poverty guidelines suggest. The United Way ALICE Project is a grassroots movement stimulating a fresh, nonpartisan national dialogue about how to reverse the trend and improve conditions for this growing population of families living paycheck to paycheck