22 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…
Janet van Niekerk, Haakon Bakka, Håvard Rue
Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. Many of these resulted in new R packages and new formulations of the joint model.…
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…
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…
Sezen Cekic, Stephen Aichele, Andreas M. Brandmaier, Ylva Köhncke + 1 more
'Paolo Ghisletta'] In biostatistics and medical research, longitudinal data are often composed of repeated assessments of a variable (e.g., blood pressure or other biomarkers) and dichotomous indicators to mark an event of interest (e.g., recovery from disease, or death). Consequently, joint modeling of longitudinal…
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…
Carmen Armero
This paper takes a quick look at Bayesian joint models (BJM) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution. Next a basic BJM…
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…
Dimitris Rizopoulos
Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markon chain Monte Carlo algorithms. JMbayes…
Devin S. Johnson, Elizabeth H. Sinclair
We present a method for modeling multiple species distributions simultaneously using Dirichlet Process random effects to cluster species into guilds. Guilds are ecological groups of species that behave or react similarly to some environmental conditions. By modeling latent guild structure, we capture the…
Reza Hashemi, Taban Baghfalaki, Viviane Philipps, Hélène Jacqmin‐Gadda
model averaging approach Authors: ['Reza Hashemi' 'Taban Baghfalaki' 'Viviane Philipps' 'Hélène Jacqmin‐Gadda'] Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal…
Nicole S. Erler, Dimitris Rizopoulos, Emmanuel Lesaffre
Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modelling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In settings involving non-linear associations or interactions, however…
Hosein Mirazi, Scott T. Wood
Osteoarthritis (OA) is a multifactorial joint disease driven by complex interactions among chondrocytes, osteoblasts, fibroblasts, and immune cells across cartilage, bone, and synovial tissues. Conventional monoculture systems are unable to capture this crosstalk, limiting their physiological relevance. Building on our…
Clara Schüler, Philipp Berger, Charlotte Grosse Wiesmann
A pivotal developmental milestone is reached around 9 months when infants begin to coordinate their attention with others. Joint attention acts as a catalyst for infants’ learning and is proposed to predict later social cognitive development, including understanding others’ minds (Theory of Mind, ToM). However, neural…
Alan E. Gelfand, Shinichiro Shirota
Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture species dependence through an associated correlation matrix arising from a…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Authors not listed
Active learning is an emerging paradigm used to help accelerating drug discovery, but most prior applications seek solely to optimize potency, whereas multiple properties influence a compound’s utility as a drug candidate. We introduce a method for multiobjective ligand optimization, which is able to efficiently handle…
Authors not listed
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
Peter Sagmeister, Lukas Melnizky, Jason Williams, C. Oliver Kappe
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies en-compass prolonged timelines for reaction and analytical model developments. Both…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…