Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models.

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Amerindian ancestry proportion as a risk factor for inflammatory bowel diseases: results from a Latin American Andean cohort.

Background and aims: Latin American populations remain underrepresented in genetic studies of inflammatory bowel diseases (IBDs). Most genetic association studies of IBD rely on Caucasian, African, and Asian individuals. These associations have yet to be evaluated in detail in the Andean region of South America. We explored the contribution of IBD-reported genetic risk variants to a Chilean cohort and the ancestry contribution to IBD in this cohort. Methods: A total of 192 Chilean IBD patients were genotyped using Illumina's Global Screening Array. Genotype data were combined with similar information from 3,147 Chilean controls. The proportions of Aymara, African, European, and Mapuche ancestries were estimated using the software ADMIXTURE. We calculated the odds ratios (ORs) and 95% confidence intervals (CIs) for gender, age, and ancestry proportions. We also explored associations with previously reported IBD-risk variants independently and in conjunction with genetic ancestry. Results: The first and third quartiles of the proportion of Mapuche ancestry in IBD patients were 24.7 and 34.2%, respectively, and the corresponding OR was 2.30 (95%CI 1.52-3.48) for the lowest vs. the highest group. Only one variant (rs7210086) of the 180 reported IBD-risk SNPs was associated with IBD risk in the Chilean cohort (adjusted P = 0.01). This variant is related to myeloid cells. Conclusion: The type and proportion of Native American ancestry in Chileans seem to be associated with IBD risk. Variants associated with IBD risk in this Andean region were related to myeloid cells and the innate immune response.

New insights from GWAS on BMI-related growth traits in a longitudinal cohort of admixed children with Native American and European ancestry.

Body-mass index (BMI) is a hallmark of adiposity. In contrast with adulthood, the genetic architecture of BMI during childhood is poorly understood. The few genome-wide association studies (GWAS) on children have been performed almost exclusively in Europeans and at single ages. We performed cross-sectional and longitudinal GWAS for BMI-related traits on 904 admixed children with mostly Mapuche Native American and European ancestries. We found regulatory variants of the immune gene HLA-DQB3 strongly associated with BMI at 1.5 - 2.5 years old. A variant in the sex-determining gene DMRT1 was associated with the age at adiposity rebound (Age-AR) in girls (P = 9.8 × 10 - 9 ). BMI was significantly higher in Mapuche than in Europeans between 5.5 and 16.5 years old. Finally, Age-AR was significantly lower (P = 0.004 ) by 1.94 years and BMI at AR was significantly higher (P = 0.04 ) by 1.2 kg/ m 2 , in Mapuche children compared with Europeans.

Ethnicity influences phenotype and clinical outcomes: Comparing a South American with a North American inflammatory bowel disease cohort.

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn disease (CD), has emerged as a global disease with an increasing incidence in developing and newly industrialized regions such as South America. This global rise offers the opportunity to explore the differences and similarities in disease presentation and outcomes across different genetic backgrounds and geographic locations. Our study includes 265 IBD patients. We performed an exploratory analysis of the databases of Chilean and North American IBD patients to compare the clinical phenotypes between the cohorts. We employed an unsupervised machine-learning approach using principal component analysis, uniform manifold approximation, and projection, among others, for each disease. Finally, we predicted the cohort (North American vs Chilean) using a random forest. Several unsupervised machine learning methods have separated the 2 main groups, supporting the differences between North American and Chilean patients with each disease. The variables that explained the loadings of the clinical metadata on the principal components were related to the therapies and disease extension/location at diagnosis. Our random forest models were trained for cohort classification based on clinical characteristics, obtaining high accuracy (0.86 = UC; 0.79 = CD). Similarly, variables related to therapy and disease extension/location had a high Gini index. Similarly, univariate analysis showed a later CD age at diagnosis in Chilean IBD patients (37 vs 24; P = .005). Our study suggests a clinical difference between North American and Chilean IBD patients: later CD age at diagnosis with a predominantly less aggressive phenotype (39% vs 54% B1) and more limited disease, despite fewer biological therapies being used in Chile for both diseases.

Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables.

Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for extracting personal information regarding this phenomenon. However, statistical modeling for these surveys is not straightforward, as the response variable is often not explicit. Hence, we propose a set of methodological procedures to deal with pro-environmental behavior data. First, validity evidence through a factorial analysis. Second, indexes are created from factor scores, where one of the latent factors summarizes a target variable. Third, a Beta regression is used to model the index of interest. Fourth, the inferential process is performed from a Bayesian perspective, in which posterior probabilities are used to sort and select the relevant variables. Finally, suitable models are obtained, and conclusions can be drawn from them. As a motivation, we used data from two Chilean surveys to illustrate our methodology as well as interpret and discuss the results.

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain.

Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer .

A tractable Bayesian joint model for longitudinal and survival data.

We introduce a numerically tractable formulation of Bayesian joint models for longitudinal and survival data. The longitudinal process is modeled using generalized linear mixed models, while the survival process is modeled using a parametric general hazard structure. The two processes are linked by sharing fixed and random effects, separating the effects that play a role at the time scale from those that affect the hazard scale. This strategy allows for the inclusion of nonlinear and time-dependent effects while avoiding the need for numerical integration, which facilitates the implementation of the proposed joint model. We explore the use of flexible parametric distributions for modeling the baseline hazard function which can capture the basic shapes of interest in practice. We discuss prior elicitation based on the interpretation of the parameters. We present an extensive simulation study, where we analyze the inferential properties of the proposed models, and illustrate the trade-off between flexibility, sample size, and censoring. We also apply our proposal to two real data applications in order to demonstrate the adaptability of our formulation both in univariate time-to-event data and in a competing risks framework. The methodology is implemented in rstan.

A strategy to impute age at onset of a particular condition from external sources.

A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition is usually unknown. Therefore, we developed a multiple imputation methodology for the age at onset of a particular condition, which is supported by incidence data from different sources of information. We introduced and illustrated such a methodology using simulated data in order to examine the performance of our proposal. Then, we analyzed the association of gallstones and fatty liver disease in the Maule Cohort, a Chilean study of chronic diseases, using participants' risk factors and six sources of information for the imputation of the age-occurrence of gallstones. Simulated studies showed that an increase in the proportion of imputed data does not affect the quality of the estimated coefficients associated with fully observed variables, while the imputed variable slowly reduces its effect. For the Chilean study, the categorized exposure time to gallstones is a significant variable, in which participants who had short and long exposure have, respectively, 26.2% and 29.1% higher chance of getting a fatty liver disease than non-exposed ones. In conclusion, our multiple imputation approach proved to be quite robust both in the linear/logistic regression simulation studies and in the real application, showing the great potential of this methodology.

Bayesian survival analysis with BUGS.

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.

Bayesian regularization for flexible baseline hazard functions in Cox survival models.

Fully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi-parametric choices allow for more flexible patterns but they can suffer from overfitting and instability. Regularization methods through prior distributions with correlated structures usually give reasonable answers to these types of situations. We discuss Bayesian regularization for Cox survival models defined via flexible baseline hazards specified by a mixture of piecewise constant functions and by a cubic B-spline function. For those "semi-parametric" proposals, different prior scenarios ranging from prior independence to particular correlated structures are discussed in a real study with microvirulence data and in an extensive simulation scenario that includes different data sample and time axis partition sizes in order to capture risk variations. The posterior distribution of the parameters was approximated using Markov chain Monte Carlo methods. Model selection was performed in accordance with the deviance information criteria and the log pseudo-marginal likelihood. The results obtained reveal that, in general, Cox models present great robustness in covariate effects and survival estimates independent of the baseline hazard specification. In relation to the "semi-parametric" baseline hazard specification, the B-splines hazard function is less dependent on the regularization process than the piecewise specification because it demands a smaller time axis partition to estimate a similar behavior of the risk.

Promoting critical thinking in an online, project-based course.

Education institutions are expected to contribute to the development of students' critical thinking skills. Due to COVID-19, there has been a surge in interest in online teaching. The aim of this study is therefore to design a strategy to promote critical thinking in an online setting for first year undergraduates. An intervention was carried out with 834 students at an engineering school; it comprised five activities designed to develop critical thinking. Both the control and experimental groups worked with a project-based learning strategy, while the experimental group was provided with scaffolding for a socially shared regulation process. All students answered an identical pre- and post-test so as to analyze the impact on critical thinking. Both strategies performed significantly better on the post-test, suggesting that online project-based learning improves critical thinking. However, following a socially shared regulation scaffolding led to a significantly greater improvement. In this sense, the socially shared regulation scaffolding provided to the experimental group proved to be key, while feedback was also an important element in the development of critical thinking. This study shows that online project-based learning fosters the development of critical thinking, while providing a socially shared regulation scaffolding also has a significant impact.

Propiedades psicométricas de la versión chilena del cuestionario Quick Disabilities of the Arm, Shoulder and Hand en pacientes con patologías de hombro / Psychometric properties of the Chilean version of the Quick Disabilities of the Arm, Shoulder and Hand (Quick DASH) questionnaire for patients with shoulder disorders

INTRODUCCIÓN: Los cuestionarios basados en la percepción subjetiva del paciente sobre las disfunciones asociadas a su patología son comúnmente utilizado como instrumentos de evaluación, para definir manejo terapéutico y evaluar estados de avance tanto en la clínica como investigación. Es relevante que los cuestionarios seleccionados midan lo que proponen de manera válida y confiable, pero que además sea factible de utilizar considerando su simpleza como el tiempo empleado en su uso. Existen diversos cuestionarios comúnmente utilizados en las patologías de hombro. Entre estos se encuentra el cuestionario Quick Disabilities of the Arm, Shoulder and Hand (Quick DASH) que puede ser utilizado en diversas disfunciones de extremidad superior y está clasificado entre los mejores cuestionarios subjetivos autoadministrados considerando sus propiedades psicométricas. Las potenciales ventajas de este instrumento incluyen el menor tiempo necesario para contestarlo y la eliminación de algunos ítems menos relevantes. La validez transcultural para la versión chilena del Quick DASH ya ha sido desarrollada, pero sus propiedades psicométricas aún no han sido estudiadas en la población chilena. OBJETIVO: Determinar la consistencia interna, confiabilidad test-retest, cambio mínimo detectable, cambio mínimo importante, cambio clínico relevante y sensibilidad del cuestionario subjetivo Quick DASH en pacientes con patologías de hombro en la población chilena. MATERIAL Y MÉTODO: 81 pacientes con patologías de hombro fueron reclutados completando el Quick DASH en 3 ocasiones. Tras visitar al médico tratante, cuando comiencen su rehabilitación kinésica y tras completar 10 sesiones de kinesioterapia. RESULTADOS: El cuestionario muestra una consistencia interna de 0.92, confiabilidad test-retest de 0.95 (0.91-0.97), cambio mínimo detectable de 19.6 %, cambio mínimo importante de 25.5%, cambio clínico relevante de 37.1% y tamaño del efecto de 1.1. CONCLUSIONES: Las propiedades psicométricas demuestran que el Quick DASH puede ser usado de manera confiable tanto en clínica como en investigación para pacientes chilenos con patologías de hombro

INTRODUCTION: The literature provides psychometric properties Quick Disabilities of the Arm, Shoulder and Hand (DASH) similar to the original DASH. The potential advantages of this instrument include the shorter time needed to answer it and the elimination of some less relevant items. The cross-cultural validity for the Chilean version of the Quick DASH has already been developed, but its psychometric properties have not yet been studied in the Chilean population. AIM: To determine the internal consistency, test-retest reliability, minimum detectable change, minimum important change, relevant clinical change, and sensitivity of the Quick DASH subjective questionnaire in patients with common shoulder pathologies in the Chilean population. MATERIAL AND METHOD: 81 patients with shoulder pathologies were recruited by completing the Quick DASH on 3 occasions. After visiting their attending physician, starting physical therapy, and after completing 10 sessions of physical therapy. RESULTS: The questionnaire shows an internal consistency of 0.92, test-retest reliability of 0.95 (0.91-0.97), minimum detectable change of 19.6%, minimum important change of 25.5%, relevant clinical change of 37.1%, and effect size (sensitivity) of 1.1. CONCLUSIONS: The psychometric properties described show that the Quick DASH can be used reliably in both clinical and research for Chilean patients with shoulder pathologies

Relationship between marital status and incidence of type 2 diabetes mellitus in a Brazilian rural population: The Baependi Heart Study.

Many factors influence the incidence of type 2 diabetes mellitus (T2DM). Here, we investigated the associations between socio-demographic characteristics and familial history with the 5-year incidence of T2DM in a family-based study conducted in Brazil. T2DM was defined as baseline fasting blood glucose ≥ 126 mg/dL or the use of any hypoglycaemic drug. We excluded individuals with T2DM at baseline or if they did not attend two examination cycles. After exclusions, we evaluated a sample of 1,125 participants, part of the Baependi Heart Study (BHS). Mixed-effects logistic regression models were used to assess T2DM incident given different characteristics. At the 5-year follow-up, the incidence of T2DM was 6.7% (7.2% men and 6.3% women). After adjusting for age, sex, and education status, the model that combined marital and occupation status, skin color, and familial history of T2DM provided the best prediction for T2DM incidence. Only marital status was independently associated with T2DM incidence. Individuals that remained married, despite having significantly increased their weight, were significantly less likely to develop diabetes than their divorced counterparts.

Association between ambient beta particle radioactivity and lower hemoglobin concentrations in a cohort of elderly men.

Although ionizing radiation is known to have detrimental effects on red blood cells, the effect of environmental radioactivity associated with ambient particulate matter (PM) is unknown. We hypothesized that exposure to ambient PM-associated beta particle radioactivity (PRß) would be associated with a lower hemoglobin concentration. We studied 1.704 participants from the Normative Aging Study (NAS) over 36 years (1981-2017) who lived in Eastern, MA and the surrounding area. Exposures to PRß was assessed using USEPA's RadNet monitoring network that measures gross beta radiation associated with ambient PM. Mixed effect models with a random intercept adjusting for potential confounders was used, including ambient black carbon (BC) and particulate matter ≤2.5 µm (PM2.5) concentrations. Greater cumulative PRß activities at 7-, 14-, 21- and 28-days before the hemoglobin determination were associated with lower hemoglobin concentrations. The greatest effect was for a 28-day moving average. An IQR of 0.83 × 10-4 Bq/m3 of ambient PRß was associated with a 0.12 g/dL decrease in hemoglobin concentration (95%CI: -0.18 to -0.05). The effects of PRß were similar when the models were adjusted for ambient BC or PM2.5. This is the first study to demonstrate an association between environmental ionizing radiation released from particulate matter with a lower hemoglobin concentration, suggesting that ambient radiation may contribute to the development of anemia.

Cardiometabolic risk factors correlated with the incidence of dysglycaemia in a Brazilian normoglycaemic sample: the Baependi Heart Study cohort.

BACKGROUND: Dysglycaemia is defined by elevated glucose levels in the blood, commonly characterized by impaired fasting glucose, impaired glucose tolerance, elevated glycated haemoglobin, or diabetes mellitus (DM) diagnosis. The abnormal levels of glucose may occur many years before DM, a condition known as prediabetes, which is correlated with comorbidities such as cardiovascular diseases. Therefore, the aim of this study was to investigate the incidence of prediabetic dysglycaemia and its relationship with cardiometabolic risk factors at a 5-year follow-up, based on an initially normoglycaemic sample in the Baependi Heart Study cohort. METHODS: The data used comes from the Baependi Heart Study cohort, which consists of two periods: cycle 1 (2005-2006) and cycle 2 (2010-2013). For this study, we excluded those who had fasting blood glucose ≥ 100 mg/dL or were taking anti-diabetic medications at baseline, and those that had diabetes diagnosed in cycle 2. Mixed-effects logistic regression models were used to assess the association between cardiometabolic risk factors and the incidence of dysglycaemia, including a familiar random effect such as a cluster. RESULTS: The incidence of prediabetic dysglycaemia was 12.8%, and it did not differ between men and women (14.4% and 11.6%, respectively). Two models were analysed to investigate the relationship between cardiometabolic risk factors and the occurrence of prediabetic dysglycaemia. The model that better explained the occurrence of dysglycaemia over the 5 years, after correction, included the waist circumference (WC) (measures and Δ), systolic blood pressure (SBP), HDL-c levels, and age. Although sex was not associated with the incidence of dysglycaemia, women and men showed differences in cardiometabolic risk factors related to glucose impairment: men who developed dysglycaemia showed, in parallel, higher LDL-c levels, TC/HDL-c ratio and DBP measurements; while these parameters remained similar between women who developed dysglycaemia and dysglycaemia-free women, after 5 years. CONCLUSIONS: In an initially normoglycaemic sample of a highly mixed population living in a traditional Brazilian lifestyle, important cardiometabolic risk factors were associated with the occurrence of prediabetic dysglycaemia, and this relationship appeared to be more important in men. These results provide important insights about cardiovascular risk in prediabetic individuals.

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior.

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

Geomagnetic disturbances driven by solar activity enhance total and cardiovascular mortality risk in 263 U.S. cities.

BACKGROUND: Short-term geomagnetic disturbances (GMD) driven by the quasi-periodic 11-year cycle of solar activity have been linked to a broad range of adverse health effects, including cardiovascular diseases (CVD) and total deaths. We conducted a large epidemiological study in 263 U.S. cities to assess the effects of GMD on daily deaths of total, CVD, myocardial infarction (MI), and stroke. METHODS: We employed a two-step meta-analysis approach, in which we estimated city-specific and season-stratified mortality risk associated with a GMD parameter (Kp index) in 263 U.S. cities. In addition, sensitivity analysis was performed to assess whether effect modification of particulate matter (PM2.5) in the prior day changed Kp index effects on daily deaths after adjusting for confounders. RESULTS: We found significant association between daily GMD and total, CVD, and MI deaths. The effects were even stronger when we adjusted the models for 24-h PM2.5 for different seasons. For example, in the winter and fall one standard deviation of z-score Kp index increase was associated with a 0.13 and 0.31% increase in total deaths, respectively (Winter: p = 0.01, 95% CI: 0.02 to 0.24; Fall: p = 0.00001; 95% CI: 0.23 to 0.4), without adjusting for PM2.5. The effects of GMD on total deaths were also observed in spring and summer in the models without PM2.5 (p = 0.00001). When the models were adjusted for PM2.5 the total deaths increased 0.47% in winter (p = 0.00001, 95% CI: 0.3 to 0.65) and by 0.23% in fall (p = 0.001, 95% CI: 0.09 to 0.37). The effects of GMD were also significant associated with MI deaths and CVD. No positive significant association were found between Kp and stroke. The GMD effects on deaths were higher than for 24 h-PM2.5 alone, especially in spring and fall. CONCLUSION: Our results suggest that GMD is associated with total, CVD and MI deaths in 263 U. S cities. Increased mortality in the general population during GMD should be further investigated to determine whether those human physiological dynamics driven by variations in solar activity can be related to daily clinical cardiovascular observations.

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

S. Typhimurium virulence changes caused by exposure to different non-thermal preservation treatments using C. elegans.

The aims of this research study were: (i) to postulate Caenorhabditis elegans (C. elegans) as a useful organism to describe infection by Salmonella enterica serovar Typhimurium (S. Typhimurium), and (ii) to evaluate changes in virulence of S. Typhimurium when subjected repetitively to different antimicrobial treatments. Specifically, cauliflower by-product infusion, High Hydrostatic Pressure (HHP), and Pulsed Electric Fields (PEF). This study was carried out by feeding C. elegans with different microbial populations: E. coli OP50 (optimal conditions), untreated S. Typhimurium, S. Typhimurium treated once and three times with cauliflower by-product infusion, S. Typhimurium treated once and four times with HHP and S. Typhimurium treated once and four times with PEF. Bayesian survival analysis was applied to estimate C. elegans lifespan when fed with the different microbial populations considered. Results showed that C. elegans is a useful organism to describe infection by S. Typhimurium because its lifespan was reduced when it was infected. In addition, the application of antimicrobial treatments repetitively generated different responses: when cauliflower by-product infusion and PEF treatment were applied repetitively the virulence of S. Typhimurium was lower than when the treatment was applied once. In contrast, when HHP treatment was applied repetitively, the virulence of S. Typhimurium was higher than when it was applied once. Nevertheless, in all the populations analyzed treated S. Typhimurium had lower virulence than untreated S. Typhimurium.

Bayesian joint modeling of bivariate longitudinal and competing risks data: An application to study patient-ventilator asynchronies in critical care patients.

Mechanical ventilation is a common procedure of life support in intensive care. Patient-ventilator asynchronies (PVAs) occur when the timing of the ventilator cycle is not simultaneous with the timing of the patient respiratory cycle. The association between severity markers and the events death or alive discharge has been acknowledged before, however, little is known about the addition of PVAs data to the analyses. We used an index of asynchronies (AI) to measure PVAs and the SOFA (sequential organ failure assessment) score to assess overall severity. To investigate the added value of including the AI, we propose a Bayesian joint model of bivariate longitudinal and competing risks data. The longitudinal process includes a mixed effects model for the SOFA score and a mixed effects beta regression model for the AI. The survival process is defined in terms of a cause-specific hazards model for the competing risks death or alive discharge. Our model indicates that the SOFA score is strongly related to vital status. PVAs are positively associated with alive discharge but there is not enough evidence that PVAs provide a more accurate indication of death prognosis than the SOFA score alone.