A Joint Model for Longitudinal and Time-to-event Data in Social and Life Course Research: Employment Status and Time to Retirement

Longitudinal studies including a time-to-event outcome in social research often use a form of event history analysis to analyse the influence of time-varying endogenous covariates on the time-to-event outcome. Many standard event history models however assume the covariates of interest to be exogenous and inclusion of an endogenous covariate may lead to bias. Although such bias can be dealt with by using joint models for longitudinal and time-to-event outcomes, these types of models are underused in social research. In order to fill this gap in the social science modelling toolkit, we introduce a novel Bayesian joint model in which a multinomial longitudinal outcome is modelled simultaneously with a time-to-event outcome. The methodological novelty of this model is that it concerns a correlated random effects association structure that includes a multinomial longitudinal outcome. We show the use of the joint model on Danish labour market data and compare the joint model to a standard event history model. The joint model has three advantages over a standard survival model. It decreases bias, allows us to explore the relation between exogenous covariates and the longitudinal outcome and can be flexibly extended with multiple time-to-event and longitudinal outcomes.

Weft Yarn Interlacement Modelling for 3D Profiled Structures

Currently, generation of 3D woven T-joint models with complex weave geometries, using TexGen software, is a manual process. One of the main challenges to automatic generation of these textiles is the order in which the weft yarns interlace within the bifurcation region. This paper will demonstrate a method for predicting the order, based on the pattern draft and the information contained within it such as the direction of weft insertion and the beating action of the loom. The path of the entangling weft yarns and the yarn cross section orientation can then be modelled. Finally, a geometric transformation is applied to simulate the opening of the flanges so that the final model reflects the T-shaped profile.

DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation

Determining causal effects of interventions onto outcomes from observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects. We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased estimation even when one of the two is misspecified. DR-VIDAL uses a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; then, an information-theoretic generative adversarial network (Info-GAN) is used to generate counterfactuals; finally, a doubly robust block incorporates propensity matching/weighting into predictions. On synthetic and real-world datasets, DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, performant framework. Code is available at: https://bitbucket.org/goingdeep2406/dr-vidal/src/master/

Cognition-Mortality Associations Are Stronger When Estimated Jointly in Longitudinal and Time-to-Event Models

Objectives With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. Methods We applied each of the above analytic approaches to data from a large-sample repeated-measures study of older adults (N = 5,954, of whom 4,453 deceased; ages 50–87 years at assessment). Results Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were stronger when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation, confirming the greater power of this statistical approach. Discussion These results suggest that joint estimation of cognition-mortality associations may be beneficial for research in cognitive epidemiology and cognitive reserve in adult development.

The role of propagule pressure and environmental factors on the establishment of a large invasive cyprinid: black carp in the Laurentian Great Lakes basin

Understanding the factors underlying species establishment is critical for the management of invasive fishes, yet the roles of propagule pressure and environmental factors are infrequently quantified in joint models. We estimated the establishment likelihood of the invasive black carp (Mylopharyngodon piceus) by examining the relative influence of propagule pressure (introduction size and age structure) and environmental factors (temperature-driven young-of-year [YOY] overwinter survival, adult survival, age at maturity, and longevity). Simulations demonstrated that both propagule pressure and environmental factors can act as non-linear bottlenecks to establishment. When the model was applied to 12 Great Lakes tributaries and nearshore areas, black carp establishment was probable with sufficient propagules and under most environmental conditions (median p = 0.21–0.73, 0.70–1.00, and 0.46–0.97 for 100 pairs of age 4, age 9, and age 16 fish, respectively), except for YOY (p < 0.01). Our analysis is one of the few studies to examine the relative role of propagule pressure and environmental conditions on establishment, indicating that both factors can lead to establishment failure independently or concurrently within an ecosystem.

Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review

Objective The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as “longitudinal”, “trajector*” and “cardiovasc*” respectively. Studies were filtered to meet the following inclusion criteria: longitudinal individual patient data in adult patients with ≥3 time-points and a CVD or mortality outcome. Studies were screened and analyzed by one author. Any queries were discussed with the other authors. Comparisons were made between the methods identified looking at assumptions, flexibility and software availability. Results From the initial 2601 studies returned by the searches 80 studies were included. Four statistical approaches were identified for modelling the longitudinal data: 3 (4%) studies compared time points with simple statistical tests, 40 (50%) used single-stage approaches, such as including single time points or summary measures in survival models, 29 (36%) used two-stage approaches including an estimated longitudinal parameter in survival models, and 8 (10%) used joint models which modelled the longitudinal and survival data together. The proportion of CVD risk prediction models created using longitudinal data using two-stage and joint models increased over time. Conclusions Single stage models are still heavily utilized by many CVD risk prediction studies for modelling longitudinal data. Future studies should fully utilize available longitudinal data when analyzing CVD risk by employing two-stage and joint approaches which can often better utilize the available data.

Association of Time–Varying Intensity of Ventilation With Mortality in Patients With COVID−19 ARDS: Secondary Analysis of the PRoVENT–COVID Study

Background: High intensity of ventilation has an association with mortality in patients with acute respiratory failure. It is uncertain whether similar associations exist in patients with acute respiratory distress syndrome (ARDS) patients due to coronavirus disease 2019 (COVID−19). We investigated the association of exposure to different levels of driving pressure (ΔP) and mechanical power (MP) with mortality in these patients.Methods: PRoVENT–COVID is a national, retrospective observational study, performed at 22 ICUs in the Netherlands, including COVID−19 patients under invasive ventilation for ARDS. Dynamic ΔP and MP were calculated at fixed time points during the first 4 calendar days of ventilation. The primary endpoint was 28–day mortality. To assess the effects of time–varying exposure, Bayesian joint models adjusted for confounders were used.Results: Of 1,122 patients included in the PRoVENT–COVID study, 734 were eligible for this analysis. In the first 28 days, 29.2% of patients died. A significant increase in the hazard of death was found to be associated with each increment in ΔP (HR 1.04, 95% CrI 1.01–1.07) and in MP (HR 1.12, 95% CrI 1.01–1.36). In sensitivity analyses, cumulative exposure to higher levels of ΔP or MP resulted in increased risks for 28–day mortality.Conclusion: Cumulative exposure to higher intensities of ventilation in COVID−19 patients with ARDS have an association with increased risk of 28–day mortality. Limiting exposure to high ΔP or MP has the potential to improve survival in these patients.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT04346342.

Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.

Joint intent detection and slot filling with wheel-graph attention networks

Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task.