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OBJECTIVE:Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials. STUDY DESIGN AND SETTING:We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model. RESULTS:Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P

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. [ABSTRACT FROM AUTHOR]

Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction. Specifically, we incorporate a multi-stage threshold selection into Q-learning to derive an interpretable form of a recommendation policy for behavioral coaching. We apply this method on an observational dataset that contains Fitbit ActiGraph measurements and self-reported stress levels. The estimated policy is then used to understand how exercise patterns may affect users' psychological stress levels and to perform coaching more effectively.

Background: African-American women develop more aggressive breast cancers and at an earlier age compared with Caucasian women., Materials and Methods: We compared gene expression profiles of breast cancer cell lines that were developed from African-American and Caucasian patients to identify biological differences in breast cancers that develop in these groups. Real-time PCR was used to evaluate mRNA expression in cell lines and in a series of breast cancer cases. Gene microarray signal intensities were also analyzed in the International Genomics Consortium Expression Project for Oncology (expO) dataset., Results: 17,-Hydroxysteroid dehydrogenase type 2 (17HSD 2) gene and mRNA expression were significantly higher in the African-American cell lines (p<0.05). However, 17HSD 2 expression did not differ significantly between the two cohorts in either our clinical series or the expO dataset. 17HSD 2 expression was found to be predictive of younger age at diagnosis and estrogen receptor status., Conclusion: Overexpression of 17HSD 2 in African-American breast cancer may contribute to the increased proportion of estrogen receptor-negative breast cancers and worse clinical outcome among African-American patients.