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

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

A protocol for emulating a published randomised controlled trial using registry data: effects of azithromycin in young adults with cystic fibrosis

Target trial emulation can be used to evaluate the effects of treatments using observational data. The trial emulation approach involves specifying key elements of a protocol for a target trial (a randomised controlled trial designed to address the question of interest) and then describing how best to emulate the trial using observational data. Recent years have seen an uptake of target trial emulation in several disease areas, although there are limited examples in cystic fibrosis. This protocol describes a study which aims to assess the applicability of target trial emulation in cystic fibrosis (CF). We aim to emulate an existing trial in CF and assess to what extent the results from the trial can be replicated using registry data. We aim to emulate a published trial (i.e., the target trial) which found evidence for beneficial effects of azithromycin use on lung function in young adults with cystic fibrosis. Two emulated trials are planned: one using data from the UK CF Registry and one using data from the US CF Registry. The inclusion and exclusion criteria, treatment and outcome definitions, follow-up period, and estimand of interest are all designed to match the published trial as closely as possible. Inverse-probability-of-treatment weighting will be used in the emulated trials to account for confounding bias. Results obtained in the emulated trials using registry data will be compared to the results obtained in the published randomised controlled trial

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

Efficacy of BETTER transitional care intervention for diverse patients with traumatic brain injury and their families: Study protocol of a randomized controlled trial

Objective The purpose of this study is to examine the efficacy of BETTER (Brain Injury, Education, Training, and Therapy to Enhance Recovery) vs. usual transitional care management among diverse adults with traumatic brain injury (TBI) discharged home from acute hospital care and families. Methods This will be a single-site, two-arm, randomized controlled trial (N = 436 people, 218 patient/family dyads, 109 dyads per arm) of BETTER, a culturally- and linguistically-tailored, patient- and family-centered, TBI transitional care intervention for adult patients with TBI and families. Skilled clinical interventionists will follow a manualized protocol to address patient/family needs. The interventionists will co-establish goals with participants; coordinate post-hospital care, services, and resources; and provide patient/family education and training on self- and family-management and coping skills for 16 weeks following hospital discharge. English- and Spanish-speaking adult patients with mild-to-severe TBI who are discharged directly home from the hospital without inpatient rehabilitation or transfer to other settings (community discharge) and associated family caregivers are eligible and will be randomized to treatment or usual transitional care management. We will use intention-to-treat analysis to determine if patients receiving BETTER have a higher quality of life (primary outcome, SF-36) at 16-weeks post-hospital discharge than those receiving usual transitional care management. We will conduct a descriptive, qualitative study with 45 dyads randomized to BETTER, using semi-structured interviews, to capture perspectives on barriers and facilitators to participation. Data will be analyzed using conventional content analysis. Finally, we will conduct a cost/budget impact analysis, evaluating differences in intervention costs and healthcare costs by arm. Discussion Findings will guide our team in designing a future, multi-site trial to disseminate and implement BETTER into clinical practice to enhance the standard of care for adults with TBI and families. The new knowledge generated will drive advancements in health equity among diverse adults with TBI and families. Trial registration NCT05929833

Predicting lung function decline in cystic fibrosis:the impact of initiating ivacaftor therapy

BACKGROUND: Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake.METHODS: We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level.RESULTS: Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with standard deviations of 0.29 and 0.20. The predictive accuracy was robust across different covariate specifications.CONCLUSIONS: The methods and applications of dynamic prediction models developed using data prior to modulator uptake have the potential to inform post-modulator projections of lung function and enhance clinical surveillance in the new era of CF care.</p

Seasonal variation of lung function in cystic fibrosis: Longitudinal modeling to compare a Midwest US cohort to international populations

Characterizing seasonal trend in lung function in individuals with chronic lung disease may lead to timelier treatment of acute respiratory symptoms and more precise distinction between seasonal exposures and variability. Limited research has been conducted to assess localized seasonal fluctuation in lung function decline in individuals with cystic fibrosis (CF) in context with routinely collected demographic and clinical data. We conducted a longitudinal cohort study of 253 individuals aged 6–22 years with CF receiving care at a pediatric Midwestern US CF center with median (range) of follow-up time of 4.7 (0–9.95) years, implementing two distinct models to estimate seasonality effects. The outcome, lung function, was measured as percent-predicted of forced expiratory volume in 1 s (FEV1). Both models showed that older age, being male, using Medicaid insurance and having Pseudomonas aeruginosa infection corresponded to accelerated FEV1 decline. A sine wave model for seasonality had better fit to the data, compared to a linear model with categories for seasonality. Compared to international cohorts, seasonal fluctuations occurred earlier and with greater volatility, even after adjustment for ambient temperature. Average lung function peaked in February and dipped in August, and FEV1 fluctuation was 0.81% predicted (95% CI: 0.52 to 1.1). Adjusting for temperature shifted the peak and dip to March and September, respectively, and decreased FEV1 fluctuation to 0.45% predicted (95% CI: 0.08 to 0.82). Understanding localized seasonal variation and its impact on lung function may allow researchers to perform precision public health for lung diseases and disorders at the point-of-care level