Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately.

Melding wildlife surveys to improve conservation inference.

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty.

A Bayesian joint model for compositional mediation effect selection in microbiome data.

Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional structure of the data and overdispersion. In practice, researchers are often interested in investigating how the microbiome may mediate the relation between an assigned treatment and an observed phenotypic response. Existing approaches designed for compositional mediation analysis are unable to simultaneously determine the presence of direct effects, relative indirect effects, and overall indirect effects, while quantifying their uncertainty.

A Bayesian zero-inflated Dirichlet-multinomial regression model for multivariate compositional count data.

The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the data as well as overdispersion. A major limitation of the DM distribution is that it is unable to handle excess zeros typically found in practice which may bias inference.

Infinite hidden Markov models for multiple multivariate time series with missing data.

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies.

Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.

Intensive longitudinal data collected with ecological momentary assessment methods capture information on participants' behaviors, feelings, and environment in near real-time. While these methods can reduce recall biases typically present in survey data, they may still suffer from other biases commonly found in self-reported data (e.g.

A BAYESIAN TIME-VARYING EFFECT MODEL FOR BEHAVIORAL MHEALTH DATA.

The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data (ILD) using ecological momentary assessment methods, which aim to capture psychological, emotional, and environmental factors that may relate to a behavioral outcome in near real-time. In order to investigate ILD collected in a novel, smartphone-based smoking cessation study, we propose a Bayesian variable selection approach for time-varying effect models, designed to identify dynamic relations between potential risk factors and smoking behaviors in the critical moments around a quit attempt.

MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.

Background: Understanding the relation between the human microbiome and modulating factors, such as diet, may help researchers design intervention strategies that promote and maintain healthy microbial communities. Numerous analytical tools are available to help identify these relations, oftentimes via automated variable selection methods. However, available tools frequently ignore evolutionary relations among microbial taxa, potential relations between modulating factors, as well as model selection uncertainty.

A Systematic Review and Meta-Analysis of Decompression Sickness in Altitude Physiological Training.

A review of decompression sickness (DCS) cases associated with the NASA altitude physiological training (APT) program at the Johnson Space Center (JSC) motivated us to place our findings into the larger context of DCS prevalence from other APT centers. We reviewed JSC records from 1999 to 2016 and 14 publications from 1968 to 2004 about DCS prevalence in other APT programs. We performed a meta-analysis of 15 APT profiles (488 cases / 385,116 exposures).

Dietary acid load and bone turnover during long-duration spaceflight and bed rest.

Background: Bed rest studies document that a lower dietary acid load is associated with lower bone resorption.

Objective: We tested the effect of dietary acid load on bone metabolism during spaceflight.

Design: Controlled 4-d diets with a high or low animal protein-to-potassium (APro:K) ratio (High and Low diets, respectively) were given to 17 astronauts before and during spaceflight.

The Time-Varying Relations Between Risk Factors and Smoking Before and After a Quit Attempt.

Introduction: Intensive longitudinal data (ILD) collected with ecological momentary assessments (EMAs) can provide a rich resource for understanding the relations between risk factors and smoking in the time surrounding a cessation attempt.

Methods: Participants (N = 142) were smokers seeking treatment at a safety-net hospital smoking cessation clinic who were randomly assigned to receive standard clinic care (ie, counseling and cessation medications) or standard care plus small financial incentives for biochemically confirmed smoking abstinence. Participants completed EMAs via study provided smartphones several times per day for 14 days (1 week prequit through 1 week postquit).

Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation.

The application of sophisticated analytical methods to intensive longitudinal data, collected with ecological momentary assessments (EMA), has helped researchers better understand smoking behaviors after a quit attempt. Unfortunately, the wealth of information captured with EMAs is typically underutilized in practice. Thus, novel methods are needed to extract this information in exploratory research studies.

Hemoglobin Oxygen Saturation with Mild Hypoxia and Microgravity.

Introduction: Microgravity (μG) exposure and even early recovery from μG in combination with mild hypoxia may increase the alveolar-arterial oxygen (O2) partial pressure gradient.

Methods: Four male astronauts on STS-69 (1995) and four on STS-72 (1996) were exposed on Earth to an acute sequential hypoxic challenge by breathing for 4 min 18.0%, 14.