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The cumulative incidence function (CIF) displays key information in the competing risks setting, which is common in medical research. In this article, we introduce two new methods to compute non-parametric confidence intervals for the CIF. First, we introduce non-parametric profile-likelihood confidence intervals. The method builds on constrained non-parametric maximum likelihood estimation (NPMLE), for which we derive closed-form formulas. This method can be seen as an extension of that of Thomas and Grunkemeier (J Am Stat Assoc 70:865-871, 1975) to the competing risks setting, when the CIF is of interest instead of the survival function. Second, we build on constrained NPMLE to introduce constrained bootstrap confidence intervals. This extends an interesting approach introduced by Barber and Jennison (Biometrics 52:430-436, 1999) to the competing risks setting. A simulation study illustrates how these methods can perform as compared to benchmarks implemented in popular software. The results suggest that more accurate confidence intervals than usual Wald-type ones can be obtained in the case of small to moderate sample sizes and few observed events. An application to melanoma data is provided for illustration purpose.

Patients receiving psychiatric medication, like the antipsychotic drug haloperidol, are at an increased risk of sudden cardiac death (SCD). Haloperidol blocks the cardiac rapidly-activating delayed rectifier potassium current, thereby increasing electrical dispersion of repolarization which can potentially lead to arrhythmias. Whether these patients are also at a higher risk to develop SCD during an acute myocardial infarction (AMI) is unknown. AMI locally shortens action potential duration, which might further increase repolarization dispersion and increase the risk of arrhythmia in the presence of haloperidol compared to without. Our aim was to test whether treatment with haloperidol implies an increased risk of SCD when eventually experiencing AMI. Twenty-eight female Danish Landrace pigs were randomized into three groups: low dose haloperidol (0.1 mg/kg), high dose (1.0 mg/kg) or vehicle-control group. One hour after haloperidol/vehicle infusion, AMI was induced by balloon-occlusion of the mid-left anterior descending coronary artery and maintained for 120 min, followed by 60 min of reperfusion. VF occurred during occlusion in 7/11 pigs in the control group, 3/11 in the low dose (p = 0.198) and 2/6 in the high dose group (p = 0.335). High dose haloperidol significantly prolonged QT, and reduced heart rate, vascular resistance and blood pressure before and during AMI. Premature ventricular contractions in phase 1b during AMI were reduced with high dose haloperidol. AMI-induced arrhythmia was not aggravated in pigs with haloperidol treatment. Our results do not suggest that AMI is contributing to the excess mortality in patients treated with antipsychotic drugs seen in epidemiological studies., (© 2019 The Authors.)

Aims: To investigate whether the recent improvements in pre-hospital cardiac arrest-management and survival following out-of-hospital cardiac arrest (OHCA) also apply to OHCA patients with psychiatric disorders., Methods: We identified all adult Danish patients with OHCA of presumed cardiac cause, 2001-2015. Psychiatric disorders were defined by hospital diagnoses up to 10 years before OHCA and analyzed as one group as well as divided into five subgroups (schizophrenia-spectrum disorders, bipolar disorder, depression, substance-induced mental disorders, other psychiatric disorders). Association between psychiatric disorders and pre-hospital OHCA-characteristics and 30-day survival were assessed by multiple logistic regression., Results: Of 27,523 OHCA-patients, 4772 (17.3%) had a psychiatric diagnosis. Patients with psychiatric disorders had lower odds of 30-day survival (0.37 95% confidence interval 0.32-0.43) compared with other OHCA-patients. Likewise, they had lower odds of witnessed status (0.75 CI 0.70-0.80), bystander cardiopulmonary resuscitation (CPR) (0.77 CI 0.72-0.83), shockable heart rhythm (0.37 95% CI, 0.33-0.40), and return of spontaneous circulation (ROSC) at hospital arrival (0.66 CI 0.59-0.72). Similar results were seen in all five psychiatric subgroups. The difference in 30-day survival between patients with and without psychiatric disorders increased in recent years: from 8.4% (CI 7.0-10.0%) in 2006 to 13.9% (CI 12.4-15.4%) in 2015 and from 7.0% (4.3-10.8%) in 2006 to 7.0% (CI 4.5-9.7%) in 2015, respectively., Conclusion: Patients with psychiatric disorders have lower survival following OHCA compared to non-psychiatric patients and the gap between the two groups has widened over time., (Copyright © 2019. Published by Elsevier B.V.)

Aims: Sudden cardiac death in the young (SCDY) accounts for a significant proportion of deaths among the young. The aim of this nationwide study was to examine temporal changes in incidence and causes of SCDY in Denmark in 2000-2009. During this 10-year period, several public and private health initiatives were undertaken to decrease morbidity and mortality in Denmark., Methods and Results: All deaths among persons aged 1-35 years in Denmark in 2000-2009 (23.7 million person-years) were included. Death certificates, autopsy reports, discharge summaries, and data from nationwide administrative registries were used to identify SCDY cases. Sudden cardiac death in the young incidence rates were age-adjusted and sex-adjusted using direct standardization. Temporal changes in standardized SCDY incidence rates were reported as average annual percent changes. In the 10-year study period, there were 8756 deaths, of which we identified 635 (7%) SCDY cases. For these SCDY cases, median age at death was 29 years and 68% were men. Standardized incidence of SCDY decreased from 3.1 per 100 000 person-years in 2000 [95% confidence interval (CI) 2.4-3.8] to 2.5 per 100 000 person-years in 2009 (95% CI 1.9-3.2). This corresponds to an average annual percent change of -3.0% (95% CI -5.8 to -0.1). The distribution of major causes of SCDY did not change significantly throughout the study period., Conclusion: Incidence of SCDY decreased significantly from 2000 through 2009 in Denmark with an average annual percent change of -3%. Further research is needed to elucidate underlying causes of this development., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.

Objective: We aimed to study whether visit-to-visit variability of glycated hemoglobin A 1c (HbA 1c ) is associated with incident major adverse cardiovascular events (MACE), all-cause mortality, and type 2 diabetes in people without diabetes., Research Design and Methods: We included primary care patients with no history of diabetes or cardiovascular disease and with three annual HbA 1c measurements within normal range (<6.5% [48 mmol/mol]). For each individual, we measured the HbA 1c variability as the SD of the residuals obtained from a linear regression on the three HbA 1c measurements. From the linear regression, we also obtained the estimated index HbA 1c (intercept) and the trend over time (slope). Follow-up began at the date of the third measurement. Associations between HbA 1c variability and outcome were analyzed using Cox regression, adjusted for traditional risk factors, intercept, and trend and reported as hazard ratio per SD increase in variability (HR SD )., Results: In total, 6,756 individuals were included. During a median follow-up time of 6.3 years, 996 developed MACE, 856 died, and 1,267 developed type 2 diabetes. We found a significant association between increasing HbA 1c variability and incident MACE (HR SD 1.09 [95% CI 1.03-1.15]) and all-cause mortality (HR SD 1.13 [95% CI 1.07-1.20]), whereas there were no associations with type 2 diabetes (HR SD 1.00 [95% CI 0.95-1.05]). We calculated 5-year absolute risks of MACE and all-cause mortality and found clinically relevant differences across several age, sex, comorbidity, and HbA 1c variability-defined subgroups., Conclusions: In a primary care population free of diabetes and cardiovascular disease, high HbA 1c variability was associated with increased risks of MACE and all-cause mortality., (© 2018 by the American Diabetes Association.)

Background: We conducted a meta-analysis in nonmetastatic breast cancer patients treated by neoadjuvant chemotherapy (NCT) to assess the clinical validity of circulating tumor cell (CTC) detection as a prognostic marker., Methods: We collected individual patient data from 21 studies in which CTC detection by CellSearch was performed in early breast cancer patients treated with NCT. The primary end point was overall survival, analyzed according to CTC detection, using Cox regression models stratified by study. Secondary end points included distant disease-free survival, locoregional relapse-free interval, and pathological complete response. All statistical tests were two-sided., Results: Data from patients were collected before NCT (n = 1574) and before surgery (n = 1200). CTC detection revealed one or more CTCs in 25.2% of patients before NCT; this was associated with tumor size (P < .001). The number of CTCs detected had a detrimental and decremental impact on overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P < .001), but not on pathological complete response. Patients with one, two, three to four, and five or more CTCs before NCT displayed hazard ratios of death of 1.09 (95% confidence interval [CI] = 0.65 to 1.69), 2.63 (95% CI = 1.42 to 4.54), 3.83 (95% CI = 2.08 to 6.66), and 6.25 (95% CI = 4.34 to 9.09), respectively. In 861 patients with full data available, adding CTC detection before NCT increased the prognostic ability of multivariable prognostic models for overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P = .008)., Conclusions: CTC count is an independent and quantitative prognostic factor in early breast cancer patients treated by NCT. It complements current prognostic models based on tumor characteristics and response to therapy.

In the context of chronic diseases, patient's health evolution is often evaluated through the study of longitudinal markers and major clinical events such as relapses or death. Dynamic predictions of such types of events may be useful to improve patients management all along their follow-up. Dynamic predictions consist of predictions that are based on information repeatedly collected over time, such as measurements of a biomarker, and that can be updated as soon as new information becomes available. Several techniques to derive dynamic predictions have already been suggested, and computation of dynamic predictions is becoming increasingly popular. In this work, we focus on assessing predictive accuracy of dynamic predictions and suggest that using an R 2 -curve may help. It facilitates the evaluation of the predictive accuracy gain obtained when accumulating information on a patient's health profile over time. A nonparametric inverse probability of censoring weighted estimator is suggested to deal with censoring. Large sample results are provided, and methods to compute confidence intervals and bands are derived. A simulation study assesses the finite sample size behavior of the inference procedures and illustrates the shape of some R 2 -curves which can be expected in common settings. A detailed application to kidney transplant data is also presented., (Copyright © 2017 John Wiley & Sons, Ltd.)

In renal transplantation, serum creatinine (SCr) is the main biomarker routinely measured to assess patient's health, with chronic increases being strongly associated with long-term graft failure risk (death with a functioning graft or return to dialysis). Joint modeling may be useful to identify the specific role of risk factors on chronic evolution of kidney transplant recipients: some can be related to the SCr evolution, finally leading to graft failure, whereas others can be associated with graft failure without any modification of SCr. Sample data for 2749 patients transplanted between 2000 and 2013 with a functioning kidney at 1-year post-transplantation were obtained from the DIVAT cohort. A shared random effect joint model for longitudinal SCr values and time to graft failure was performed. We show that graft failure risk depended on both the current value and slope of the SCr. Deceased donor graft patient seemed to have a higher SCr increase, similar to patient with diabetes history, while no significant association of these two features with graft failure risk was found. Patient with a second graft was at higher risk of graft failure, independent of changes in SCr values. Anti-HLA immunization was associated with both processes simultaneously. Joint models for repeated and time-to-event data bring new opportunities to improve the epidemiological knowledge of chronic diseases. For instance in renal transplantation, several features should receive additional attention as we demonstrated their correlation with graft failure risk was independent of the SCr evolution.