JM: An R package for the joint modelling of longitudinal and time-to-event data

In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal a

An alternative characterization of MAR in shared parameter models for incomplete longitudinal data and its utili

Dropout is a common complication in longitudinal studies, especially since the distinction between missing not at random (MNAR) and missing at random (MAR) dropout is intractable. Consequently, one starts with an analysis that is valid under MAR and then performs a sensitivity analysis by considering MNAR departures from it. To this end, specific classes of joint models, such as pattern-mixture models (PMMs) and selection models (SeMs), have been proposed. On the contrary, shared-parameter models (SPMs) have received less attention, possibly because they do not embody a characterization of MAR. A few approaches to achieve MAR in SPMs exist, but are difficult to implement in existing software. In this article, we focus on SPMs for incomplete longitudinal and time-to-dropout data and propose an alternative characterization of MAR by exploiting the conditional independence assumption, under which outcome and missingness are independent given a set of random effects. By doing so, the censoring distribution can be utilized to cover a wide range of assumptions for the missing data mechanism on the subject-specific level. This approach offers substantial advantages over its counterparts and can be easily implemented in existing software. More specifically, it offers flexibility over the assumption for the missing data generating mechanism that governs dropout by allowing subject-specific perturbations of the censoring distribution, whereas in PMMs and SeMs dropout is considered MNAR strictly

Uncertainty in Semantic Schema Integration

In this paper we present a new method of semantic schema integration, based on uncertain semantic mappings. The purpose of semantic schema integration is to produce a unified representation of multiple data sources. First, schema matching is performed to identify the semantic mappings between the schema objects. Then, an integrated schema is produced during the schema merging process based on the identified mappings. If all semantic mappings are known, schema merging can be performed (semi-)automatically

Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature

We propose a new method to perform approximate likelihood inference in latent variable models. Our approach provides an approximation of the integrals involved in the likelihood function through a reduction of their dimension that makes the computation feasible in situations in which classical and adaptive quadrature based methods are not applicable. We derive new theoretical results on the accuracy of the obtained estimators. We show that the proposed approximation outperforms several existing methods in simulations, and it can be successfully applied in presence of multidimensional longitudinal data when standard techniques are not applicable or feasible

Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically “emptying” superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s (FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied

Bayesian imputation of time-varying covariates in linear mixed models

Studies involving large observational datasets commonly face the challenge of dealing with multiple missing values. The most popular approach to overcome this challenge, multiple imputation using chained equations, however, has been shown to be sub-optimal in complex settings, specifically in settings with longitudinal outcomes, which cannot be easily and adequately included in the imputation models. Bayesian methods avoid this difficulty by specification of a joint distribution and thus offer an alternative. A popular choice for that joint distribution is the multivariate normal distribution. In more complicated settings, as in our two motivating examples that involve time-varying covariates, additional issues require consideration: the endo- or exogeneity of the covariate and its functional relation with the outcome. In such situations, the implied assumptions of standard methods may be violated, resulting in bias. In this work, we extend and study a more flexible, Bayesian alternative to the multivariate normal approach, to better handle complex incomplete longitudinal data. We discuss and compare assumptions of the two Bayesian approaches about the endo- or exogeneity of the covariates and the functional form of the association with the outcome, and illustrate and evaluate consequences of violations of those assumptions using simulation studies and two real data examples

Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach

Joint models for longitudinal and survival data have gained a lot of attention in recent years, with the development of myriad extensions to the basic model, including those which allow for multivariate longitudinal data, competing risks and recurrent events. Several software packages are now also available for their implementation. Although mathematically straightforward, the inclusion of multiple longitudinal outcomes in the joint model remains computationally difficult due to the large number of random effects required, which hampers the practical application of this extension. We present a novel approach that enables the fitting of such models with more realistic computational times. The idea behind the approach is to split the estimation of the joint model in two steps: estimating a multivariate mixed model for the longitudinal outcomes and then using the output from this model to fit the survival submodel. So-called two-stage approaches have previously been proposed and shown to be biased. Our approach differs from the standard version, in that we additionally propose the application of a correction factor, adjusting the estimates obtained such that they more closely resemble those we would expect to find with the multivariate joint model. This correction is based on importance sampling ideas. Simulation studies show that this corrected two-stage approach works satisfactorily, eliminating the bias while maintaining substantial improvement in computational time, even in more difficult settings

High Impact of Pediatric Inflammatory Bowel Disease on Caregivers' Work Productivity and Daily Activities: An International Prospective Study

To evaluate the longitudinal evolution of work productivity loss and activity impairment in caregivers of children with inflammatory bowel disease (IBD). We also evaluated the associations between these impairments, IBD-related factors, and caregivers’ health-related quality of life (HRQOL) and estimated the indirect costs related to work absenteeism. Study design Since January 2017, children with newly diagnosed IBD were enrolled prospectively in the Pediatric Inflammatory Bowel Disease Network for Safety, Efficacy, Treatment and Quality improvement of care study. The impact of pediatric-onset IBD on caregivers' socioeconomic functioning (work and daily activities) and HRQOL was assessed using the Work Productivity and Activity Impairment for caregivers questionnaire and the European Quality of Life Five Dimension Five Level questionnaire, at diagnosis and 3 and 12 months of age. Generalized estimating equation models were applied to evaluate outcomes longitudinally, adjusted for IBD type, disease activity, and child's age at diagnosis. Results Up to July 2021, 491 children with IBD were eligible for analysis of caregivers' Work Productivity and Activity Impairment questionnaire. At diagnosis, the mean caregivers' employment rate was 78.4%; the adjusted mean work productivity loss was 44.6% (95% CI, 40.2%-49.0%), and the adjusted mean activity impairment was 34.3% (95% CI, 30.8%-37.7%). Work productivity loss and activity impairment significantly decreased over time and were associated with disease activity, but not with IBD type or child's age. Caregivers' HRQOL was associated with both impairments. Costs related to work absenteeism were at least €6272 ($7276) per patient during the first year after diagnosis. Conclusions Caregivers of children with IBD experience significant impairments in work and daily activities, especially at diagnosis. The impact decreases thereafter and is associated with disease activity and caregivers’ HRQOL. Work absenteeism results in high indirect costs