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The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy. [ABSTRACT FROM AUTHOR]

Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. Clinical trials are an essential part of clinical research programs that aim to improve therapies and reduce the burden of disease. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. When designing clinical trials, investigators make many decisions regarding various aspects of how they will carry out the study, such as the primary objective of the study, primary and secondary endpoints, methods of analysis, sample size, etc. This article provides a brief review of the clinical development of new treatments and argues for the use of Bayesian methods and decision theory in clinical research. [ABSTRACT FROM AUTHOR]

Busulfan (Bu)/cyclophosphamide (Cy) is a standard conditioning platform for allogeneic transplantation. We developed a strategy separating the Cy into two pre/post-transplantation doses (PTCy), providing myeloablative conditioning and single-agent graft-versus-host disease (GVHD) prophylaxis. We investigated the impact of Bu route on treatment-related toxicity for 131 consecutive adult patients. Busulfan was administered in four daily divided doses either orally (n = 72) or intravenously (n = 59) with pharmacokinetics on the first-dose and as necessary on subsequent doses to achieve a target area-under-the-concentration-curve (AUC) of 800-1400 µmol*min/L per dose. BuCy/PTCy with pharmacokinetics is well-tolerated with low treatment-related toxicity. Hepatic veno-occlusive disease incidence was 6% with two fatal events. Bu administration route in the context of BuCy/PTCy did not statistically impact hepatotoxicity, GVHD, relapse, disease-free survival, or overall survival. The BuCy/PTCy platform has a low incidence of treatment-related toxicity, including hepatotoxicity, in hematologic malignancies when using pharmacokinetics for a target AUC of 800-1400 µmol*min/L, irrespective of Bu administration route. [ABSTRACT FROM AUTHOR]

The optimal treatment for chronic myeloid leukemia (CML) relapsing following allogeneic bone marrow transplant (alloBMT) is unknown. We performed a single-center retrospective analysis of 71 consecutive patients undergoing alloBMT for CML from 1995 to 2008. A multi-state model was used to quantify cumulative incidences of complete molecular response (CMR) and death following alloBMT. The primary analysis was comparison of three treatment interventions (tyrosine kinase inhibitor: TKI, donor lymphocyte infusion: DU, and TKI + DU) for relapsed disease post-alloBMT. Forty-five (63%) patients relapsed postalloBMT (molecular relapse: n = 16, cytogenetic relapse: n = 20, hematologic relapse: n = 2, advanced phase relapse: n = 7) and 40 patients underwent one of three treatments: TKI-only (n = 13), DU-only (n = 11) or TKI + DU (n = 16). Although not statistically significant, the TKI-only group had the highest cumulative incidence of CMR and lowest cumulative incidence of death compared to DU and TKI + DU. These data support the finding that TKI therapy is active in the post-alloBMT setting. [ABSTRACT FROM AUTHOR]

The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliable estimators at the company level even when there are only a few bonds per company. After describing the proposed model, we discuss an empirical application in which the term structure of 197 individual companies is estimated. The sample of 197 consists of 143 companies with only one or two bonds. In-sample and out-of-sample tests are used to quantify the improvement in accuracy that results from approximating the term structure of corporate bonds with estimators by company rather than by credit rating, the latter being a popular choice in the financial literature. A complete description of a Markov chain Monte Carlo (MCMC) scheme for the proposed model is available as Supplementary Material. [ABSTRACT FROM AUTHOR]

We consider dependent nonparametric models for related random probability distributions. For example, the random distributions might be indexed by a categorical covariate indicating the treatment levels in a clinical trial and might represent random effects distributions under the respective treatment combinations. We propose a model that describes dependence across random distributions in an analysis of variance (ANOVA)-type fashion. We define a probability model in such a way that marginally each random measure follows a Dirichlet process (DP) and use the dependent Dirichlet process to define the desired dependence across the related random measures. The resulting probability model can alternatively be described as a mixture of ANOVA models with a DP prior on the unknown mixing measure. The main features of the proposed approach are ease of interpretation and computational simplicity. Because the model follows the standard ANOVA structure, interpretation and inference parallels conventions for ANOVA models. This includes the notion of main effects, interactions, contrasts, and the like. Of course, the analogies are limited to structure and interpretation. The actual objects of the inference are random distributions instead of the unknown normal means in standard ANOVA models. Besides interpretation and model structure, another important feature of the proposed approach is ease of posterior simulation. Because the model can be rewritten as a DP mixture of ANOVA models, it inherits all computational advantages of standard DP mixture models. This includes availability of efficient Gibbs sampling schemes for posterior simulation and ease of implementation of even high-dimensional applications. Complexity of implementing posterior simulation is-at least conceptually--dimension independent. [ABSTRACT FROM AUTHOR]

We propose a model for longitudinal data with random effects that includes model-based smoothing of measurements over time. This research is motivated by experiments evaluating the hemodynamic effects of various agents in tumor-bearing rats. In one set of experiments, the rats breathed room air. Followed by carbogen (a mixture of pure oxygen and carbon dioxide). The experimental responses are longitudinal measurements of oxygen pressure measured in tissue, tumor blood flow, and mean arterial pressure. The nature of the recorded responses does not allow any meaningful parametric form to model these profiles over time. Additionally, response patterns differ widely across individuals. Therefore, we propose a nonparametric regression to model the profile data over time. We propose a dynamic state-space model to smooth the data at the profile level. Using the state parameters, we formally define "change" in the measured responses. A hierarchical extension allows inference to include a regression on covariates. The proposed approach provide a modeling framework for any longitudinal data, where no parsimonious parametric model is available at the level of the repeated measurements and a hierarchical modeling of some feature of a smooth fit for these profiles data is desired. The proposed MCMC algorithm for inference on the hierarchical extension is appropriate in any hierarchical model in which posterior simulation for the submodels is significantly easier. [ABSTRACT FROM AUTHOR]

Population pharmacokinetic and pharmacodynamic studies require analyzing nonlinear growth curves fit to multiple measurements from study subjects. We propose a class of nonlinear population models with nonparametric second-stage priors for analyzing such data. The proposed models apply a flexible class of mixtures to implement the nonparametric second stage. The discussion is based on a pharmacodynamic study involving longitudinal data consisting of hematologic profiles (i.e., blood counts measured over time) of cancer patients undergoing chemotherapy. We describe a full posterior analysis in a Bayesian framework. This includes prediction of future observations (profiles and end points for new patients), estimation of the mean response function for observed individuals, and inference on population characteristics. The mixture model is specified and given a hyperprior distribution by means of a Dirichlet processes prior on the mixing measure. Estimation is implemented by a combination of various Markov chain Monte Carlo schemes, including a novel independence chain scheme for a logistic regression. The discussion is motivated by a pharmacodynamic case study; however, the concepts are more generally applicable to the wider class of population models. [ABSTRACT FROM AUTHOR]