Community Directed Leadership Programs in Wyoming

Efforts to increase local support for community leadership programs have led Wyoming to develop a very heavy emphasis on local direction and delivery. The Wyoming concept features programs led by a volunteer steering committee and guided by general parameters

Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data

The joint modeling of longitudinal and time-to-event outcomes has become a popular tool infollow-up studies. However, fitting Bayesian joint models to large datasets, such as patientregistries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resultingMarkov chain Monte Carlo draws into a consensus distribution. We used a simulation studyto investigate how different consensus strategies perform with joint models. In particular,we compared grouping all draws together with using equal- and precision-weighted averages.We considered scenarios reflecting different sample sizes, numbers of data splits, and processor characteristics. Parallelization of the sampling process substantially decreased the timerequired to run the model. We found that the weighted-average consensus distributions forlarge sample sizes were nearly identical to the target posterior distribution. The proposedalgorithm has been made available in an R package for joint models, JMbayes2. This workwas motivated by the clinical interest in investigating the association between ppFEV1, acommonly measured marker of lung function, and the risk of lung transplant or death, using data from the US Cystic Fibrosis Foundation Patient Registry (35,153 individuals with372,366 years of cumulative follow-up). Splitting the registry into five subsamples resultedin an 85% decrease in computing time, from 9.22 to 1.39 hours. Splitting the data and finding a consensus distribution by precision-weighted averaging proved to be a computationallyefficient and robust approach to handling large datasets under the joint modeling framework

Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data

The joint modeling of longitudinal and time-to-event outcomes has become a popular tool infollow-up studies. However, fitting Bayesian joint models to large datasets, such as patientregistries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resultingMarkov chain Monte Carlo draws into a consensus distribution. We used a simulation studyto investigate how different consensus strategies perform with joint models. In particular,we compared grouping all draws together with using equal- and precision-weighted averages.We considered scenarios reflecting different sample sizes, numbers of data splits, and processor characteristics. Parallelization of the sampling process substantially decreased the timerequired to run the model. We found that the weighted-average consensus distributions forlarge sample sizes were nearly identical to the target posterior distribution. The proposedalgorithm has been made available in an R package for joint models, JMbayes2. This workwas motivated by the clinical interest in investigating the association between ppFEV1, acommonly measured marker of lung function, and the risk of lung transplant or death, using data from the US Cystic Fibrosis Foundation Patient Registry (35,153 individuals with372,366 years of cumulative follow-up). Splitting the registry into five subsamples resultedin an 85% decrease in computing time, from 9.22 to 1.39 hours. Splitting the data and finding a consensus distribution by precision-weighted averaging proved to be a computationallyefficient and robust approach to handling large datasets under the joint modeling framework

A joint model for (un)bounded longitudinal markers, competing risks, and recurrent events using patient registry data

Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval (a,b) without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2

A joint model for (un)bounded longitudinal markers, competing risks, and recurrent events using patient registry data

Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval (a,b) without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S'(t). We predict S'(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.�This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.�Steps required for mediation analyses in the seasonality model are shown.�The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study

Built environment factors predictive of early rapid lung function decline in cystic fibrosis

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. Objective: To identify built environment characteristics predictive of rapid CF lung function decline. Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [−0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. Conclusion: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions

A systematic map of studies testing the relationship between temperature and animal reproduction

Funding: This work was funded by the European Society for Evolution (which funds a Special Topic Network on Evolutionary Ecology of Thermal Fertility Limits to CF, AB, RRS and TARP), the Natural Environment Research Council (NE/P002692/1 to TARP, AB and RRS, NE/X011550/1 to LRD and TARP), the Biotechnology and \Biological Sciences Research Council (BB/W016753/1 to AB, TARP and RRS) and a Heisenberg fellowship from the German Research Foundation (FR 2973/11-1 to CF).1. Exposure to extreme temperatures can negatively affect animal reproduction, by disrupting the ability of individuals to produce any offspring (fertility), or the number of offspring produced by fertile individuals (fecundity). This has important ecological consequences, because reproduction is the ultimate measure of population fitness: a reduction in reproductive output lowers the population growth rate and increases the extinction risk. Despite this importance, there have been no large‐scale summaries of the evidence for effect of temperature on reproduction. 2. We provide a systematic map of studies testing the relationship between temperature and animal reproduction. We systematically searched for published studies that statistically test for a direct link between temperature and animal reproduction, in terms of fertility, fecundity or indirect measures of reproductive potential (gamete and gonad traits). 3. Overall, we collated a large and rich evidence base, with 1654 papers that met our inclusion criteria, encompassing 1191 species. 4. The map revealed several important research gaps. Insects made up almost half of the dataset, but reptiles and amphibians were uncommon, as were non‐arthropod invertebrates. Fecundity was the most common reproductive trait examined, and relatively few studies measured fertility. It was uncommon for experimental studies to test exposure of different life stages, exposure to short‐term heat or cold shock, exposure to temperature fluctuations, or to independently assess male and female effects. Studies were most often published in journals focusing on entomology and pest control, ecology and evolution, aquaculture and fisheries science, and marine biology. Finally, while individuals were sampled from every continent, there was a strong sampling bias towards mid‐latitudes in the Northern Hemisphere, such that the tropics and polar regions are less well sampled. 5. This map reveals a rich literature of studies testing the relationship between temperature and animal reproduction, but also uncovers substantial missing treatment of taxa, traits, and thermal regimes. This database will provide a valuable resource for future quantitative meta‐analyses, and direct future studies aiming to fill identified gaps.Publisher PDFPeer reviewe