BackgroundAvailable methods for the joint modelling of longitudinal and time-to-event outcomes have typically only allowed for a single longitudinal outcome and a solitary event time. In practice, clinical studies are likely to record multiple longitudinal outcomes. Incorporating all sources of data will improve the predictive capability of any model and lead to more informative inferences for the purpose of medical decision-making.MethodsWe reviewed current methodologies of joint modelling for time-to-event data and multivariate longitudinal data including the distributional and modelling assumptions, the association structures, estimation approaches, software tools for implementation and clinical applications of the methodologies.ResultsWe found that a large number of different models have recently been proposed. Most considered jointly modelling linear mixed models with proportional hazard models, with correlation between multiple longitudinal outcomes accounted for through multivariate normally distributed random effects. So-called current value and random effects parameterisations are commonly used to link the models. Despite developments, software is still lacking, which has translated into limited uptake by medical researchers.ConclusionAlthough, in an era of personalized medicine, the value of multivariate joint modelling has been established, researchers are currently limited in their ability to fit these models routinely. We make a series of recommendations for future research needs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0212-5) contains supplementary material, which is available to authorized users.

In a systematic review and meta-analysis of 83 prognostic studies of C-reactive protein in coronary disease, Hemingway and colleagues find substantial biases, preventing them from drawing clear conclusions relating to the use of this marker in clinical practice.

Available methods for joint modelling of longitudinal and survival data typically have only one failure type for the time to event outcome. We extend the methodology to allow for competing risks data. We fit a cause-specific hazards sub-model to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure.The method is applied to data from the SANAD trial of anti-epileptic drugs (AEDs), as a means of investigating the effect of drug titration on the relative effects of lamotrigine (LTG) and carbamazepine (CBZ) on treatment failure. Concern had been expressed that differential titration rates may have been to the disadvantage of CBZ. The beneficial effect of LTG on unacceptable adverse events leading to drug withdrawal did not lessen and indeed increased slightly when a calibrated dose was accounted for in the joint model. Adjustment for the titration rate of LTG relative to CBZ resulted in an unchanged effect of the former on drug withdrawals due to inadequate seizure control. LTG remains the AED of choice from this analysis.

BackgroundJoint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome. Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a practical algorithm for fitting the models, and demonstrate how to fit the models using a new package for the statistical software platform R, joineRML.ResultsA multivariate linear mixed sub-model is specified for the longitudinal outcomes, and a Cox proportional hazards regression model with time-varying covariates is specified for the event time sub-model. The association between models is captured through a zero-mean multivariate latent Gaussian process. The models are fitted using a Monte Carlo Expectation-Maximisation algorithm, and inferences are based on approximate standard errors from the empirical profile information matrix, which are contrasted to an alternative bootstrap estimation approach. We illustrate the model and software on a real data example for patients with primary biliary cirrhosis with three repeatedly measured biomarkers.ConclusionsAn open-source software package capable of fitting multivariate joint models is available. The underlying algorithm and source code makes use of several methods to increase computational speed.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0502-1) contains supplementary material, which is available to authorized users.

ObjectiveTo determine whether the effect of South Asian ethnicity differs between studies of incidence and prognosis of coronary disease.DesignSystematic literature review and meta-analysis, and cohort analysis from a national acute coronary syndrome (ACS) registry linked to mortality (National Institute of Cardiovascular Outcomes Research/Myocardial Infarction National Audit Project).SettingInternational for the review, and England and Wales for the cohort analysis.PatientsThe numbers of South Asians included in the meta-analysis were 111 555 (incidence) and 14 531 (prognosis) of whom 8251 were from the ACS cohort.Main outcome measuresIncidence studies: non-fatal myocardial infarction or fatal coronary heart disease; prognostic studies: mortality; HRs for 1-year all-cause death in ACS cohort.ResultsSouth Asians had higher incidence of coronary disease compared with white subjects (HR 1.35 95% CI 1.30 to 1.40) based on meta-analysis of nine studies. Among 10 studies on prognosis, South Asians had better prognosis compared with white subjects (HR 0.78 95% CI 0.74 to 0.82). In the ACS cohort, the impact of diabetes (42.4% of South Asians, 16.9% of white subjects) on 1-year mortality was stronger in South Asians than white subjects (age-adjusted HR 1.83 95% CI 1.59 to 2.11 vs 1.53 95% CI 1.49 to 1.57). However, prognosis was better in South Asians even among diabetics, older people and those living in areas of the highest social deprivation.ConclusionsSouth Asian ethnicity is associated with higher incidence of coronary disease, but lower mortality once coronary disease is manifest. The dissociation between effects on incidence and prognosis suggests that public health initiatives to reduce inequalities in mortality between South Asian and white populations should focus on primary prevention.This is a CALIBER study with ClinicalTrials.gov Identifier: NCT01163513.