BackgroundThe recent progress in medical research generates an increasing interest in the use of longitudinal biomarkers for characterizing the occurrence of an outcome. The present work is motivated by a study, where the objective was to explore the potential of the long pentraxin 3 (PTX3) as a prognostic marker of Acute Graft-versus-Host Disease (GvHD) after haematopoietic stem cell transplantation. Time-varying covariate Cox model was commonly used, despite its limiting assumptions that marker values are constant in time and measured without error. A joint model has been developed as a viable alternative; however, the approach is computationally intensive and requires additional strong assumptions, in which the impacts of their misspecification were not sufficiently studied.MethodsWe conduct an extensive simulation to clarify relevant assumptions for the understanding of joint models and assessment of its robustness under key model misspecifications. Further, we characterize the extent of bias introduced by the limiting assumptions of the time-varying covariate Cox model and compare its performance with a joint model in various contexts. We then present results of the two approaches to evaluate the potential of PTX3 as a prognostic marker of GvHD after haematopoietic stem cell transplantation.ResultsOverall, we illustrate that a joint model provides an unbiased estimate of the association between a longitudinal marker and the hazard of an event in the presence of measurement error, showing improvement over the time-varying Cox model. However, a joint model is severely biased when the baseline hazard or the shape of the longitudinal trajectories are misspecified. Both the Cox model and the joint model correctly specified indicated PTX3 as a potential prognostic marker of GvHD, with the joint model providing a higher hazard ratio estimate.ConclusionsJoint models are beneficial to investigate the capability of the longitudinal marker to characterize time-to-event endpoint. However, the benefits are strictly linked to the correct specification of the longitudinal marker trajectory and the baseline hazard function, indicating a careful consideration of assumptions to avoid biased estimates.

Aims To describe the type and the amount of formal and informal care received during the first year after home discharge and to identify the baseline predictors of the formal and informal care needs of stroke survivors. Design Longitudinal study. Data were collected between June 2013–May 2016. Methods Survivors (N = 415) were enrolled during discharge from rehabilitation hospitals and interviewed at 3 (T1), 6 (T2), 9 (T3), and 12 (T4) months. The linear mixed effects model with random intercept and random slopes was used to trend for the amount of formal and informal care received by survivors during the four observation times. Results Regarding formal care, only physiotherapy and speech therapy decreased significantly over time. Stroke survivors received a mean of 17 hr of paid informal care per week at T1 and these hours did not significantly decrease after one year from discharge, while unpaid informal care decreased significantly over time. Higher numbers of paid informal caregiving were predicted by older age, higher education levels, lower physical functioning, and living without unpaid informal caregivers while higher numbers of unpaid informal care were predicted by lower physical functioning and living with unpaid informal caregivers. Conclusions Stroke has a great effect on survivors’ lives. During the first few months after rehabilitation hospital discharge, survivors need further care because they are often discharged before achieving independent functioning. Impact The results of this study could be important to guide future interventions aimed at imporving stroke survivors' conditions after post rehabilitation hospital discharge.

Coupled atmosphere–ocean general circulation models are key tools to investigate climate dynamics and the climatic response to external forcings, to predict climate evolution and to generate future climate projections. Current general circulation models are, however, undisputedly affected by substantial systematic errors in their outputs compared to observations. The assessment of these so-called biases, both individually and collectively, is crucial for the models’ evaluation prior to their predictive use. We present a Bayesian hierarchical model for a unified assessment of spatially referenced climate model biases in a multi-model framework. A key feature of our approach is that the model quantifies an overall common bias that is obtained by synthesizing bias across the different climate models in the ensemble, further determining the contribution of each model to the overall bias. Moreover, we determine model-specific individual bias components by characterizing them as non-stationary spatial fields. The approach is illustrated based on the case of near-surface air temperature bias in the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled Model Intercomparison Project. The results demonstrate the improved quantification of the bias and interpretative advantages allowed by the posterior distributions derived from the proposed Bayesian hierarchical framework, whose generality favors its broader application within climate model assessment

Background After discharge from a rehabilitation hospital, stroke survivors and their families may face considerable stroke-related direct costs. The total amount could be ascribed to the costs of formal and informal care and to the equipment or materials needed for care. Objectives This study aims to describe the direct costs incurred after a stroke by survivors during their first poststroke year and to analyze the basic predictors of these costs. Methods Stroke survivors (N = 415) were enrolled for this study during discharge from rehabilitation hospitals (baseline) and interviewed at 3, 6, 9, and 12 months after discharge for a longitudinal study. The trend of the direct costs incurred during the follow-up (from T1 to T4; n = 239) was evaluated using a linear mixed-effects model. The mixed-effects model was used to identify the baseline predictors of the incurred direct costs from the stroke survivors. Results During the first year after discharge, stroke survivors spent approximately $3700 on stroke-related direct (ie, medical and nonmedical) costs. The highest direct costs occurred during the first 6 months, although there was not a significant change over time. The higher direct costs incurred were predicted by the linear effect of time, by the educational level (higher vs low), and by the lower Barthel Index score, whereas a higher perceived cost was predicted only by the linear effect of time and by the lower Barthel Index score. Conclusion In the first poststroke year, direct costs have remained stable over time and can be predicted by the level of education and physical functioning. The identification of specific direct cost predictors would be helpful for developing more socially and economically tailored interventions for stroke survivors in their first year after their stroke.