Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12-05

AUTHORS

Maria Sudell, Ruwanthi Kolamunnage-Dona, Catrin Tudur-Smith

ABSTRACT

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. More... »

PAGES

168

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-016-0272-6

DOI

http://dx.doi.org/10.1186/s12874-016-0272-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007681681

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27919221


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