16 papers · ranked by Valyu relevance
Hok Pan Yuen, Andrew Mackinnon, Antonio Palazón-Bru
Joint modelling has emerged to be a potential tool to analyse data with a time-to-event outcome and longitudinal measurements collected over a series of time points. Joint modelling involves the simultaneous modelling of the two components, namely the time-to-event component and the longitudinal component. The main…
Hakime Ayele Kosa, Markos Abiso Erango
Hypertension is a chronic disease that has a major health problem over the centuries due to its significant contribution to the global burden. The objective of this study was to examine the association of survival time and longitudinal Systolic Blood Pressure (SBP) measurement and finding potential barrier that affects…
Colin Griesbach, Andreas Groll, Elisabeth Bergherr
Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In…
Jil Heege, Sonja Greven, Elke Schaeffner, Ulrike Grittner + 1 more
Joint models offer an unbiased statistical approach for analyzing the effects of longitudinal biomarkers on time-to-event outcomes, providing an alternative to time-varying Cox proportional-hazards regression and the two-stage approach. However, whether available implementations of these methods perform reliably across…
Maha Alsefri, Maria Sudell, Marta García-Fiñana, Ruwanthi Kolamunnage-Dona
'Ruwanthi Kolamunnage-Dona'] Background In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint…
Hannah M. H. Dold, Ingo Fründ, Emmanuel Andreas Stamatakis
Statistical modeling produces compressed and often more easily interpretable descriptions of experimental data in form of model parameters. When experimental manipulations target selected parameters, it is necessary for their interpretation that other model components remain constant. For example, psychophysicists use…
Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, Ruwanthi Kolamunnage-Dona
'Ruwanthi Kolamunnage-Dona'] Background Available 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…
Wondwosen Kassahun-Yimer, Karen A. Valle, Adebamike A. Oshunbade, Michael E. Hall + 4 more
'Michael E. Hall' 'Yuan-I. Min' 'Loretta Cain-Shields' 'Pramod Anugu' 'Adolfo Correa'] Background Multiple longitudinal responses together with time-to-event outcome are common in biomedical studies. There are several instances where the longitudinal responses are correlated with each other and at the same time each…
Rehema K. Ouko, Mavuto Mukaka, Eric O. Ohuma
Background Joint models are powerful statistical models that allow us to define a joint likelihood for quantifying the association between two or more outcomes. Joint modelling has been shown to reduce bias in parameter estimates, increase the efficiency of statistical inference by incorporating the correlation between…
Ryan J. Field, Carly Adamson, Pardeep Jhund, Jim Lewsey
Background Joint modelling combines two or more statistical models to reduce bias and increase efficiency. As the use of joint modelling increases it is important to understand how and why it is being applied to heart failure research. Methods A systematic review of major medical databases of studies which used joint…
Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi, Gajendra K. Vishwakarma
'Gajendra K. Vishwakarma'] We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the…
Maria Sudell, Ruwanthi Kolamunnage‐Dona, François Gueyffier, Catrin Tudur Smith
'Catrin Tudur Smith'] Background: Joint modeling of longitudinal and time-to-event data is often advantageous over separate longitudinal or time-to-event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time-to-event outcomes. The…
Valeria Leiva-Yamaguchi, Danilo Alvares
Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected…
Maria Sudell, Ruwanthi Kolamunnage-Dona, Catrin Tudur-Smith
Background 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…
Konstantin G. Arbeev, Igor Akushevich, Alexander M. Kulminski, Svetlana V. Ukraintseva + 1 more
'Svetlana V. Ukraintseva' 'Anatoliy I. Yashin'] Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal…
Verrah A. Otiende, Thomas N. Achia, Henry G. Mwambi, Eric Forgoston
The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine…