Joint models for longitudinal and time to event data: a review of reporting quality with a view to meta analysis
BackgroundJoint 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.MethodsWe 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.ResultsThe 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.ConclusionsWhilst 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.
BMC Medical Research Methodology