Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results.
We undertook a literature review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made.
The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports.
Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.

BACKGROUND: Absenteeism and turnover among healthcare workers have a significant impact on overall healthcare system performance. The literature captures variables from different levels of measurement and analysis as being associated with attendance behavior among nurses. Yet, it remains unclear how variables from different contextual levels interact to impact nurses\' attendance behaviors.
OBJECTIVE: The purpose of this review is to develop an integrative multilevel framework that optimizes our understanding of absenteeism and turnover among nurses in hospital settings.
METHODS: We therefore systematically examine English-only studies retrieved from two major databases, PubMed and CINAHL Plus and published between January, 2007 and January, 2013 (inclusive).
RESULTS: Our review led to the identification of 7619 articles out of which 41 matched the inclusion criteria. The analysis yielded a total of 91 antecedent variables and 12 outcome variables for turnover, and 29 antecedent variables and 9 outcome variables for absenteeism. The various manifested variables were analyzed using content analysis and grouped into 11 categories, and further into five main factors: Job, Organization, Individual, National and inTerpersonal (JOINT). Thus, we propose the JOINT multilevel conceptual model for investigating absenteeism and turnover among nurses.
CONCLUSIONS: The JOINT model can be adapted by researchers for fitting their hypothesized multilevel relationships. It can also be used by nursing managers as a lens for holistically managing nurses\' attendance behaviors.

Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the time-to-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.