55 citations · 116 across the 17 of their papers we have counts for
6 papers · 1 filter
Optimizing Dynamic Predictions from Joint Models using Super Learning
Dimitris Rizopoulos, Jeremy M. G. Taylor
Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two com…
Personalized Biopsy Schedules Using an Interval-censored Cause-specific Joint Model
Zhenwei Yang, Dimitris Rizopoulos, Eveline A. M. Heijnsdijk +2
Active surveillance (AS), where biopsies are conducted to detect cancer progression, has been acknowledged as an efficient way to reduce the overtreatment of prostate cancer. Most…
JointAI: Joint Analysis and Imputation of Incomplete Data in R
Nicole S. Erler, Dimitris Rizopoulos, Emmanuel M. E. H. 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 specific…
Joint Models with Multiple Longitudinal Outcomes and a Time-to-Event Outcome: a Corrected Two-Stage Approach
Katya Mauff, Ewout Steyerberg, Isabella Kardys +2
Joint models for longitudinal and survival data have gained a lot of attention in recent years, with the development of myriad extensions to the basic model, including those which…
Personalized Screening Intervals for Biomarkers using Joint Models for Longitudinal and Survival Data
Dimitris Rizopoulos, Jeremy M. G. Taylor, Joost van Rosmalen +2
Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Biomarkers are one of the primarily tools used for the…
Approximate likelihood inference in generalized linear latent variable models based on integral dimension reduction
Silvia Bianconcini, Silvia Cagnone, Dimitris Rizopoulos
Latent variable models represent a useful tool for the analysis of complex data when the constructs of interest are not observable. A problem related to these models is that the in…