Methodology and Software for Joint Modelling of Time-to-Event Data and Longitudinal Outcomes Across Multiple Studies

Thesis Title: Methodology and Software for Joint Modelling of Time-to-Event Data and Longitudinal Outcomes Across Multiple Studies Author: Maria Sudell Introduction and Aims: Univariate joint models for longitudinal and time-to-event data simultaneously model one outcome that is repeatedly measured over time, with another outcome which measures the time until the occurrence of an event. They have been increasingly used in the literature to account for dropout in longitudinal studies, to include time-varying covariates in time-to-event analyses, or to investigate links between longitudinal and time-to-event outcomes. Meta-analysis is the quantitative pooling of data from multiple studies. Such analyses can provide increased sample size and so detect small covariate effects. Modelling of multi-study data requires accounting for the clustering of individuals within studies and careful consideration of heterogeneity between studies. Research concerning methodology for modelling of joint longitudinal and time-to-event data in a multi-study or meta-analytic setting does not currently exist. This thesis develops novel methodologies and software for the modelling of multi-study joint longitudinal and time-to-event data. Methods: A review of current reporting standards of analyses applying joint modelling methodology to single study datasets, with a view to future Aggregate Data Meta-Analyses (AD-MA) of joint data is undertaken. Methodology for the one and two-stage Individual Participant Data Meta-Analyses (IPD-MA) are developed. A software package in the R language containing functionalities for various aspects of multi-study joint modelling analyses is built. The methodology and software is implemented in a real hypertension dataset, and also is tested in extensive simulation studies. Results: Reporting of model structure was amongst the areas identified for improvement in the reporting of joint models employed in single study applied analyses. Sufficient information was reported in the majority of studies for them to contribute to future AD-MA. Guidelines developed to ensure good quality two-stage IPD-MA of joint data were presented, designed to ensure only parameters with comparable interpretations are pooled. A range of one-stage models, each accounting for between study heterogeneity in varying ways, were described and applied to real data and simulation analyses. Models employing study level random effects were found unreliable for the investigated association structure, however fixed effect approaches or those that stratified baseline hazard by study were more reliable. The benefit of using joint models over separate time-to-event models in the presence of significant association between the longitudinal and time-to-event outcomes in both one and two-stage analyses was established. Novel software capable of one or two-stage analyses of large multi-study joint datasets was demonstrated in both the real data and simulation analyses. Conclusions: Reporting of joint modelling structure in single study applied analyses should be maintained and improved. Two-stage meta-analyses of joint modelling results should take care to pool only parameters with comparable interpretations. In meta-analyses, investigators should employ a joint modelling approach when association is known or suspected between the longitudinal and time-to-event outcomes. Further work into meta-analytic joint models is required to expand the range of available multi-study joint modelling structures, to allow for multivariate joint data, and to employ multivariate meta-analytic techniques in a two-stage meta-analysis.