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. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced survey effort.

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. We propose a formulation of a Bayesian joint model for compositional data that allows for the identification, estimation, and uncertainty quantification of various causal estimands in high-dimensional mediation analysis. We conduct simulation studies and compare our method's mediation effects selection performance with existing methods. Finally, we apply our method to a benchmark data set investigating the sub-therapeutic antibiotic treatment effect on body weight in early-life mice.

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. To fill this gap, we propose a novel Bayesian zero-inflated DM model for multivariate compositional count data with excess zeros. We then extend our approach to regression settings and embed sparsity-inducing priors to perform variable selection for high-dimensional covariate spaces. Throughout, modeling decisions are made to boost scalability without sacrificing interpretability or imposing limiting assumptions. Extensive simulations and an application to a human gut microbiome dataset are presented to compare the performance of the proposed method to existing approaches. We provide an accompanying R package with a user-friendly vignette to apply our method to other datasets.

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. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.

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., measurement error and social desirability bias). To accommodate potential biases, we develop a Bayesian hidden Markov model to simultaneously identify risk factors for subjects transitioning between discrete latent states as well as risk factors potentially associated with them misreporting their true behaviors. We use simulated data to demonstrate how ignoring potential measurement error can negatively affect variable selection performance and estimation accuracy. We apply our proposed model to smartphone-based ecological momentary assessment data collected within a randomized controlled trial that evaluated the impact of incentivizing abstinence from cigarette smoking among socioeconomically disadvantaged adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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. RESULTS: We present MicroBVS, an R package for Dirichlet-tree multinomial models with Bayesian variable selection, for the identification of covariates associated with microbial taxa abundance data. The underlying Bayesian model accommodates phylogenetic structure in the abundance data and various parameterizations of covariates' prior probabilities of inclusion. CONCLUSION: While developed to study the human microbiome, our software can be employed in various research applications, where the aim is to generate insights into the relations between a set of covariates and compositional data with or without a known tree-like structure.

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. We use parameter-expansion and data-augmentation techniques to efficiently explore how the underlying structure of these relations varies over time and across subjects. We achieve deeper insights into these relations by introducing nonparametric priors for regression coefficients that cluster similar effects for risk factors while simultaneously determining their inclusion. Results indicate that our approach is well-positioned to help researchers effectively evaluate, design, and deliver tailored intervention strategies in the critical moments surrounding a quit attempt.

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

INTRODUCTION: 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.METHODS: 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). We used meta-regression to evaluate the relation between estimated exposures and probability of DCS in a test group, accounting for the heterogeneity between studies.RESULTS: Our in-house review identified 6 Type I DCS (1 from an inside observer) and 1 Type II DCS. There were 6 cases in 9560 student hypobaric exposures from 3 NASA training flights; a student pooled prevalence rate of 0.44 cases / 1000 exposures compared to 1.44 cases / 1000 from 12 published APT profiles. The overall pooled DCS prevalence rate was 1.16 cases / 1000 exposures. There was substantial heterogeneity in DCS prevalence across studies. Denitrogenation time, exposure pressure, and exposure time were associated with probability of DCS in the meta-regression model.CONCLUSIONS: While the overall DCS prevalence rate is relatively low, there is marked heterogeneity among profiles. The pooled DCS prevalence rate estimate for the NASA profiles was lower than the overall rate. Variability in APT profile DCS prevalence could be further explained given student level and additional test-level covariates.Conkin J, Sanders RW, Koslovsky MD, Wear ML, Kozminski AG, Abercromby AFJ. A systematic review and meta-analysis of decompression sickness in altitude physiological training. Aerosp Med Hum Perform. 2018; 89(11):941-951.

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. Each astronaut had 1 High and 1 Low diet session before flight and 2 High and 2 Low sessions during flight, in addition to a 4-d session around flight day 30 (FD30), when crew members were to consume their typical in-flight intake. At the end of each session, blood and urine samples were collected. Calcium, total protein, energy, and sodium were maintained in each crew member's preflight and in-flight controlled diets. Results: Relative to preflight values, N-telopeptide (NTX) and urinary calcium were higher during flight, and bone-specific alkaline phosphatase (BSAP) was higher toward the end of flight. The High and Low diets did not affect NTX, BSAP, or urinary calcium. Dietary sulfur and age were significantly associated with changes in NTX. Dietary sodium and flight day were significantly associated with urinary calcium during flight. The net endogenous acid production (NEAP) estimated from the typical dietary intake at FD30 was associated with loss of bone mineral content in the lumbar spine after the mission. The results were compared with data from a 70-d bed rest study, in which control (but not exercising) subjects' APro:K was associated with higher NTX during bed rest. Conclusions: Long-term lowering of NEAP by increasing vegetable and fruit intake may protect against changes in loss of bone mineral content during spaceflight when adequate calcium is consumed, particularly if resistive exercise is not being performed. This trial was registered at clinicaltrials.gov as NCT01713634.

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). EMAs assessed current contextual factors including environmental (eg, easy access to cigarettes, being around others smoking), cognitive (eg, urge to smoke, stress, coping expectancies, cessation motivation, cessation self-efficacy, restlessness), behavioral (ie, recent smoking and alcohol consumption), and affective variables. Temporal relations between risk factors and smoking were assessed using a logistic time-varying effect model. Results: Participants were primarily female (57.8%) and Black (71.8%), with an annual household income of <$20000 per year (71.8%), who smoked 17.6 cigarettes per day (SD = 8.8). Individuals assigned to the financial incentives group had decreased odds of smoking compared with those assigned to usual care beginning 3 days before the quit attempt and continuing throughout the first week postquit. Environmental, cognitive, affective, and behavioral variables had complex time-varying impacts on smoking before and after the scheduled quit attempt. Conclusions: Knowledge of time-varying effects may facilitate the development of interventions that target specific psychosocial and behavioral variables at critical moments in the weeks surrounding a quit attempt. Implications: Previous research has examined time-varying relations between smoking and negative affect, urge to smoke, smoking dependence, and certain smoking cessation therapies. We extend this work using ILD of unexplored variables in a socioeconomically disadvantaged sample of smokers seeking cessation treatment. These findings could be used to inform ecological momentary interventions that deliver treatment resources (eg, video- or text-based content) to individuals based upon critical variables surrounding their attempt.

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. One of the main objectives of intensive longitudinal data analysis is identifying relations between risk factors and outcomes of interest. Our goal is to develop and apply expectation maximization variable selection for Bayesian multistate Markov models with interval-censored data to generate new insights into the relation between potential risk factors and transitions between smoking states. Through simulation, we demonstrate the effectiveness of our method in identifying associated risk factors and its ability to outperform the LASSO in a special case. Additionally, we use the expectation conditional-maximization algorithm to simplify estimation, a deterministic annealing variant to reduce the algorithm's dependence on starting values, and Louis's method to estimate unknown parameter uncertainty. We then apply our method to intensive longitudinal data collected with EMA to identify risk factors associated with transitions between smoking states after a quit attempt in a cohort of socioeconomically disadvantaged smokers who were interested in quitting.

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%, 14.9%, 13.5%, 12.9%, and 12.2% oxygen-balance nitrogen. The 18.0% O2 mixture at sea level resulted in an inspired O2 partial pressure (PIo2) of 127 mmHg. The equivalent PIO2 was also achieved by breathing 26.5% O2 at 527 mmHg that occurred for several days in µG on the Space Shuttle. A Novametrix CO2SMO Model 7100 recorded hemoglobin (Hb) oxygen saturation through finger pulse oximetry (Spo2, %). There were 12 in-flight measurements collected. Measurements were also taken the day of (R+0) and 2 d after (R+2) return to Earth. Linear mixed effects models assessed changes in Spo2 during and after exposure to µG. RESULTS: Astronaut Spo2 levels at baseline, R+0, and R+2 were not significantly different from in flight, about 97% given a PIo2 of 127 mmHg. There was also no difference in astronaut Spo2 levels between baseline and R+0 or R+2 over the hypoxic challenge. CONCLUSIONS: The multitude of physiological changes associated with µG and during recovery from µG did not affect astronaut Spo2 under hypoxic challenge.Conkin J, Wessel JH III, Norcross JR, Bekdash OS, Abercromby AFJ, Koslovsky MD, Gernhardt ML. Hemoglobin oxygen saturation with mild hypoxia and microgravity. Aerosp Med Hum Perform. 2017; 88(6):527-534.