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. Th…
Situación de la investigación en el cribado de cáncer de mama en España: implicaciones para la prevención
ResumenObjetivoLa finalización del despliegue de los programas de detección precoz de cáncer de mama en todas las comunidades autónomas de España ha facilitado el inicio de una investigación conjunta, con información procedente de varios programas, orientada a mejorar su efectividad.MétodosSe ha construido una cohorte de más de 1 millón de mujeres cribadas para evaluar el riesgo y los determinantes de los resultados falsos positivos. Se ha evaluado el impacto del cambio de técnica de mamografía analógica a digital y se está estudiando el cáncer de intervalo y los resultados falsos negativos.ResultadosLos resultados ofrecen información muy útil, desde el punto de vista de salud pública, para…
An Ordinal Joint Model for Breast Cancer
We propose a Bayesian joint model to analyze the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model and the time-to-event process through a left-truncated Cox proportional hazards model with information of the longitudinal marker and baseline covariates. Both longitudinal and survival processes are connected by a common vector of random effects.
Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data
The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian infe…