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The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive application of this type of models has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, providing real time risk assessment using all recorded information. The aim of this paper is two-fold. First, to highlight the importance of modeling the association structure between the longitudinal and event time responses that can greatly influence the derived predictions, and second, to illustrate how we can improve the accuracy of the derived predictions by suitably combining joint models with different association structures. The second goal is achieved using Bayesian model averaging, which, in this setting, has the very intriguing feature that the model weights are not fixed but they are rather subject-and time-dependent, implying that at different follow-up times predictions for the same subject may be based on different models.

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Background: To support decision-making in aortic valve replacement in children and adults, we provide a comprehensive overview of outcome after the Ross procedure. Methods and Results: A systematic search was conducted for publications reporting clinical outcome after the Ross procedure, published between January 1, 2000, and November 22, 2017. Reported event rates and time-to-event data were pooled and entered into a microsimulation model to calculate life expectancy and lifetime event risk. Ninety-nine publications were included (13 129 patients; total follow-up: 93 408 patient-years, pooled mean follow-up: 7.9±5.3 years). Pooled mean age at surgery was 9.4±5.5 years for children and 41.9±11.4 for adults. For children and adults, respectively, pooled early mortality risk was 4.19% (95% CI, 3.21–5.46) and 2.01% (95% CI, 1.44–2.82), late mortality rate was 0.54%/y (95% CI, 0.42–0.70) and 0.59%/y (95% CI, 0.46–0.76), autograft reintervention 1.28%/y (95% CI, 0.99–1.66) and 0.83%/y (95% CI, 0.68–1.01), and right ventricular outflow tract reintervention 1.97%/y (95% CI, 1.64–2.36) and 0.47%/y (95% CI, 0.37–0.59). Pooled thromboembolism and bleeding rates were low and comparable to the general population. Lifetime risks of autograft and right ventricular outflow tract reintervention were, respectively, 94% and 100% for children and 49% and 19% for a 45-year-old. Estimated life expectancy after surgery was 59 years for children (general population: 64 years) and 30 years for a 45 years old (general population: 31 years). Conclusions: Through excellent survival and avoidance of the burden of anticoagulation, the Ross procedure provides a unique opportunity for patients whose preferences do not align with the outcome provided by mechanical valve replacement and for growing children who also benefit from autograft diameter increase along with somatic growth. On the downside, almost all pediatric and many adult Ross patients will require a reintervention in their lifetime.

BackgroundAfter potentially curative resection of primary colorectal cancer, patients may be monitored by measurement of carcinoembryonic antigen and/or CT to detect asymptomatic metastatic disease earlier.MethodsA systematic review and meta‐analysis was conducted to find evidence for the clinical effectiveness of monitoring in advancing the diagnosis of recurrence and its effect on survival. MEDLINE (Ovid), Embase, the Cochrane Library, Web of Science and other databases were searched for randomized comparisons of increased intensity monitoring compared with a contemporary standard policy after resection of primary colorectal cancer.ResultsThere were 16 randomized comparisons, 11 with published survival data. More intensive monitoring advanced the diagnosis of recurrence by a median of 10 (i.q.r. 5–24) months. In ten of 11 studies the authors reported no demonstrable difference in overall survival. Seven RCTs, published from 1995 to 2016, randomly assigned 3325 patients to a monitoring protocol made more intensive by introducing new methods or increasing the frequency of existing follow‐up protocols versus less invasive monitoring. No detectable difference in overall survival was associated with more intensive monitoring protocols (hazard ratio 0·98, 95 per cent c.i. 0·87 to 1·11).ConclusionBased on pooled data from randomized trials published from 1995 to 2016, the anticipated survival benefit from surgical treatment resulting from earlier detection of metastases has not been achieved.

This study demonstrates that outcome after mechanical AVR in non-elderly adults is characterized by suboptimal survival and considerable lifetime risk of anticoagulation-related complications, but also reoperation. Non-elderly adult patients who are facing prosthetic valve selection are entitled to conveyance of evidence-based estimates of the risks and benefits of both mechanical and biological valve options in a shared decision-making process.

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Nowadays there is an increased medical interest in personalized medicine and tailoring decision making to the needs of individual patients. Within this context our developments are motivated from a Dutch study at the Cardio-Thoracic Surgery Department of the Erasmus Medical Center, consisting of patients who received a human tissue valve in aortic position and who were thereafter monitored echocardiographically. Our aim is to utilize the available follow-up measurements of the current patients to produce dynamically updated predictions of both survival and freedom from re-intervention for future patients. In this paper, we propose to jointly model multiple longitudinal measurements combined with competing risk survival outcomes and derive the dynamically updated cumulative incidence functions. Moreover, we investigate whether different features of the longitudinal processes would change significantly the prediction for the events of interest by considering different types of association structures, such as time-dependent trajectory slopes and time-dependent cumulative effects. Our final contribution focuses on optimizing the quality of the derived predictions. In particular, instead of choosing one final model over a list of candidate models which ignores model uncertainty, we propose to suitably combine predictions from all considered models using Bayesian model averaging.