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Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the \"semi-competing risks\" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State.

In the USA, the Centers for Medicare and Medicaid Services use 30-day readmission, following hospitalization, as a proxy outcome to monitor quality of care. These efforts generally focus on treatable health conditions, such as pneumonia and heart failure. Expanding quality-of-care systems to monitor conditions for which treatment options are limited or non-existent, such as pancreatic cancer, is challenging because of the non-trivial force of mortality; 30-day mortality for pancreatic cancer is approximately 30%. In the statistical literature, data that arise when the observation of the time to some non-terminal event is subject to some terminal event are referred to as 'semicompeting risks data'. Given such data, scientific interest may lie in at least one of three areas: estimation or inference for regression parameters, characterization of dependence between the two events and prediction given a covariate profile. Existing statistical methods focus almost exclusively on the first of these; methods are sparse or non-existent, however, when interest lies with understanding dependence and performing prediction. We propose a Bayesian semiparametric regression framework for analysing semicompeting risks data that permits the simultaneous investigation of all three of the aforementioned scientific goals. Characterization of the induced posterior and posterior predictive distributions is achieved via an efficient Metropolis–Hastings–Green algorithm, which has been implemented in an R package. The framework proposed is applied to data on 16051 individuals who were diagnosed with pancreatic cancer between 2005 and 2008, obtained from Medicare part A. We found that increased risk for readmission is associated with a high comorbidity index, a long hospital stay at initial hospitalization, non-white race, being male and discharge to home care.

IntroductionStudies on the association of cancer and risk of dementia are inconclusive due to result heterogeneity and concerns of survivor bias and unmeasured confounding.MethodsThis study uses data from the Memento cohort, a French multicenter cohort following persons with either mild or isolated cognitive complaints for a median of 5 years. Illness‐death models (IDMs) were used to estimate transition‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cancer in relation to dementia from time since study entry.ResultsThe analytical sample (N = 2258) excluded 65 individuals without follow‐up information. At the end of follow‐up, 286 individuals were diagnosed with dementia, 166 with incident cancer, and 95 died. Incident cancer was associated with a reduced risk of dementia (HR = 0.58, 95% CI = 0.35‐0.97), with a corresponding E‐value of 2.84 (lower CI = 1.21).DiscussionThis study supports a protective relationship between incident cancer and dementia, encouraging further investigations to understand potential underlying mechanisms.

We suggest a wild bootstrap resampling technique for nonparametric inference on transition probabilities in a general time-inhomogeneous Markov multistate model. We first approximate the limiting distribution of the Nelson-Aalen estimator by repeatedly generating standard normal wild bootstrap variâtes, while the data is kept fixed. Next, a transformation using a functional delta method argument is applied. The approach is conceptually easier than direct resampling for the transition probabilities. It is used to investigate a non-standard time-to-event outcome, currently being alive without immunosuppressive treatment, with data from a recent study of prophylactic treatment in allogeneic transplanted leukemia patients. Due to non-monotonic outcome probabilities in time, neither standard survival nor competing risks techniques apply, which highlights the need for the present methodology. Finite sample performance of time-simultaneous confidence bands for the outcome probabilities is assessed in an extensive simulation study motivated by the clinical trial data. Example code is provided in the web-based Supplementary Materials.

In many instances, a subject can experience both a nonterminal and terminal event where the terminal event (e.g., death) censors the nonterminal event (e.g., relapse) but not vice versa. Typically, the two events are correlated. This situation has been termed semicompeting risks (e.g., Fine, Jiang, and Chappell, 2001 , Biometrika 88, 907-939; Wang, 2003 , Journal of the Royal Statistical Society, Series B 65, 257-273), and analysis has been based on a joint survival function of two event times over the positive quadrant but with observation restricted to the upper wedge. Implicitly, this approach entertains the idea of latent failure times and leads to discussion of a marginal distribution of the nonterminal event that is not grounded in reality. We argue that, similar to models for competing risks, latent failure times should generally be avoided in modeling such data. We note that semicompeting risks have more classically been described as an illness-death model and this formulation avoids any reference to latent times. We consider an illness-death model with shared frailty, which in its most restrictive form is identical to the semicompeting risks model that has been proposed and analyzed, but that allows for many generalizations and the simple incorporation of covariates. Nonparametric maximum likelihood estimation is used for inference and resulting estimates for the correlation parameter are compared with other proposed approaches. Asymptotic properties, simulations studies, and application to a randomized clinical trial in nasopharyngeal cancer evaluate and illustrate the methods. A simple and fast algorithm is developed for its numerical implementation.

Ito et al present an illness–death model projecting 82 scenarios for the prevalence of anxiety disorders in Germany from 2019 to 2030 following the COVID-19 pandemic. We suggest the modelling framework used by Ito et al has promising applications for mental health epidemiology.

In most clinical and epidemiologic studies, information on disease status is usually collected at regular follow-up visits. Often, this information can only be retrieved in individuals who are alive at follow-up, and studies frequently right censor individuals with missing information because of death in the analysis. Such ad hoc analyses can lead to seriously biased hazard ratio estimates of potential risk factors. We systematically investigate this bias. We illustrate under which conditions the bias can occur. Considering three numerical studies, we characterize the bias, its magnitude, and direction as well as its real-world relevance. Depending on the situation studied, the bias can be substantial and in both directions. It is mainly caused by differential mortality: if deaths without occurrence of the disease are more pronounced, the risk factor effect is overestimated. However, if the risk for dying after being diseased is prevailing, the effect is mostly underestimated and might even change signs. The bias is a result of both, a too coarse follow-up and an ad hoc Cox analysis in which the data sample is restricted to the observed and known event history. This is especially relevant for studies in which a considerable number of death cases are expected.

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information before the truncation time, which, when exploited, increases efficiency. They are obtained as differences between two survival functions constructed for sub-samples of subjects occupying specific states at a certain time point. Both estimators correct the oversampling of relatively large survival times by using the left-truncation times associated with the cross-sectional observation. Asymptotic results are established, and finite sample performance is investigated through simulations. One of the proposed estimators performs better when there is no censoring, while the second one is strongly recommended with censored data. The new estimators are applied to data on patients in intensive care units (ICUs).

Aims We aimed to identify prognostic individual factors in patients with first acute heart failure (HF) hospitalization, considering both death and readmission as part of the natural history of HF. Methods and results We used data from the observational, prospective, multicentre EPICAL2 cohort study from which we selected incident cases of acute HF alive at discharge. We relied on an illness‐death model to identify prognostic factors on first readmission and on mortality before and after readmission. In 451 patients hospitalized for first acute HF, we observed within the year after discharge, 23 (5.1%) deaths before readmission and 270 (59.9%) first readmissions, of which 60 (22.2%) were followed by death of any cause. First, among patient characteristics, only Charlson index ≥ 8 was associated with first readmission [adjusted hazard ratio (aHR) = 1.6, 95% confidence interval (CI) (1.1–2.3), P = 0.011]. Second, Charlson index ≥ 8 [aHR = 4.2, 95% CI (1.2–14.8), P = 0.025], low blood pressure (BP) [aHR = 12.2, 95% CI (1.9–79.6), P = 0.009], high BP [aHR = 6.9, 95% CI (1.3–36.4), P = 0.023], and prescription of recommended dual or triple HF therapy at index discharge [aHR = 0.2, 95% CI (0.1–0.7), P = 0.014] were associated with mortality before any readmission. Third, Charlson index ≥ 8 [aHR = 2.4, 95% CI (1.1–5.6), P = 0.037] and the time to first readmission (per 30 days additional) [aHR = 1.2; 95% CI (1.1–1.4), P = 0.007] were associated with mortality after readmission. Conclusions Regardless of the prognostic state considered, we showed that comorbidities are of critical prognostic value in a real‐world cohort of incident HF cases. This argues in favour of multidisciplinary care in HF.

Background Despite remarkable progress in contemporary medical technology and enhanced survival outcomes for various cancer types, pancreatic cancer (PC) continues to stand out as a particularly deadly gastrointestinal malignancy. Given a persistent rise in both incidence and the corresponding mortality rates of PC globally, evaluations of PC burden by sex are of great importance. Here, we used the illness‐death multi‐state model (IDM) to forecast the prevalence of PC through the year 2040. Methods IDM was established based on obtainable data to predict the future prevalence of PC on global, regional, and national scales from 2019 to 2040. Analyses were also performed regarding sex and 95% confidence intervals (CIs) are presented for all estimates. Results The projected prevalence rate for 2040 is anticipated to be 6.093 ([95% CI 5.47–6.786] per 100,000) worldwide, indicating a significant increase of 31.45% since 1990, and a 12.29% increase since 2019. The estimated average annual increase since 2020 was 0.5%. Considering sex differences, females are expected to have a steeper slope in prevalence rate than males. Intriguingly, when considering the percentage changes between the periods of 2019–2040 and 1990–2019 for both sexes, females exhibited 29% and 11% increase relative to males (2.6‐fold greater increase). Conclusions By 2040, it is predicted that the prevalence of PC will increase globally, with women being at higher risk of developing the disease. Considering the percentage changes, regions with lower socioeconomic status are anticipated to face a greater risk of experiencing PC compared to other geographical areas.