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

Physicians' acceptance of pharmacists' interventions in daily hospital practice

Background The physicians' acceptance rate of pharmacists' interventions to improve pharmacotherapy can vary depending on the setting. The acceptance rate of interventions proposed by pharmacists located in the hospital pharmacy over the telephone and factors associated with acceptance are largely unknown. Objective To determine the physicians' acceptance rate of pharmacists' interventions proposed over the telephone in daily hospital practice and to identify factors associated with acceptance. Setting A retrospective case-control study was performed concerning adult patients admitted to a university hospital in the Netherlands. Method Pharmacists' interventions, based on alerts for drug-drug interactions and drug dosing in patients with renal impairment, recorded between January 2012 and June 2013 that were communicated over the telephone were included. Factors associated with physicians' acceptance were identified with the use of a mixed-effects logistic model. Main outcome measure The primary outcome was the proportion of accepted interventions. Results A total of 841 interventions were included. Physicians accepted 599 interventions, resulting in an acceptance rate of 71.2%. The mixed-effects logistic model showed that acceptance was significantly associated with the number of prescribed drugs (16 to ≤ 20 drugs ORadj 1.88; 95% CI 1.05-3.35, > 20 drugs ORadj 2.90; 95% CI 1.41-5.96, compared to ≤ 10 drugs) and the severity of the drug-related problem (problem without potential harm ORadj 6.36; 95% CI 1.89-21.38; problem with potential harm OR 6.78; 95% CI 2.09-21.99, compared to clinically irrelevant problems), and inversely associated with continuation of pre-admission treatment (ORadj 0.55; 95% CI 0.35-0.87). Conclusion Over the study period, the majority of pharmacists' interventions proposed over the telephone were accepted by physicians. The probability for acceptance increased for patients with an increasing number of medication orders, for clinically relevant problems and for problems related to treatment initiated during admission

Physicians’ acceptance of pharmacists’ interventions in daily hospital practice

Background The physicians’ acceptance rate of pharmacists’ interventions to improve pharmacotherapy can vary depending on the setting. The acceptance rate of interventions proposed by pharmacists located in the hospital pharmacy over the telephone and factors associated with acceptance are largely unknown. Objective To determine the physicians’ acceptance rate of pharmacists’ interventions proposed over the telephone in daily hospital practice and to identify factors associated with acceptance. Setting A retrospective case–control study was performed concerning adult patients admitted to a university hospital in the Netherlands. Method Pharmacists’ interventions, based on alerts for drug–drug interactions and drug dosing in patients with renal impairment, recorded between January 2012 and June 2013 that were communicated over the telephone were included. Factors associated with physicians’ acceptance were identified with the use of a mixed-effects logistic model. Main outcome measure The primary outcome was the proportion of accepted interventions. Results A total of 841 interventions were included. Physicians accepted 599 interventions, resulting in an acceptance rate of 71.2%. The mixed-effects logistic model showed that acceptance was significantly associated with the number of prescribed drugs (16 to ≤ 20 drugs ORadj 1.88; 95% CI 1.05–3.35, > 20 drugs ORadj 2.90; 95% CI 1.41–5.96, compared to ≤ 10 drugs) and the severity of the drug-related problem (problem without potential harm ORadj 6.36; 95% CI 1.89–21.38; problem with potential harm OR 6.78; 95% CI 2.09–21.99, compared to clinically irrelevant problems), and inversely associated with continuation of pre-admission treatment (ORadj 0.55; 95% CI 0.35–0.87). Conclusion Over the study period, the majority of pharmacists’ interventions proposed over the telephone were accepted by physicians. The probability for acceptance increased for patients with an increasing number of medication orders, for clinically r

Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures

The joint modeling of longitudinal and survival data has recently received much attention. Several extensions of the standard joint model that consists of one longitudinal and one survival outcome have been proposed including the use of different association structures between the longitudinal and the survival outcomes. However, in general, relatively little attention has been given to the selection of the most appropriate functional form to link the two outcomes. In common practice, it is assumed that the underlying value of the longitudinal outcome is associated with the survival outcome. However, it could be that different characteristics of the patients' longitudinal profiles influence the hazard. For example, not only the current value but also the slope or the area under the curve of the longitudinal outcome. The choice of which functional form to use is an important decision that needs to be investigated because it could influence the results. In this paper, we use a Bayesian shrinkage approach in order to determine the most appropriate functional forms. We propose a joint model that includes different association structures of different biomarkers and assume informative priors for the regression coefficients that correspond to the terms of the longitudinal process. Specifically, we assume Bayesian lasso, Bayesian ridge, Bayesian elastic net, and horseshoe. These methods are applied to a dataset consisting of patients with a chronic liver disease, where it is important to investigate which characteristics of the biomarkers have an influence on survival. Copyright (C) 2016 John Wiley & Sons, Ltd