60 citations · 71 across the 8 of their papers we have counts for
10 papers
latrend: A Framework for Clustering Longitudinal Data
Niek Den Teuling, Steffen Pauws, Edwin van den Heuvel
Clustering of longitudinal data is used to explore common trends among subjects over time for a numeric measurement of interest. Various R packages have been introduced throughout…
Beyond Conditional Averages: Estimating The Individual Causal Effect Distribution
Richard Post, Edwin van den Heuvel
In recent years, the field of causal inference from observational data has emerged rapidly. The literature has focused on (conditional) average causal effect estimation. When (rema…
Flexible machine learning estimation of conditional average treatment effects: a blessing and a curse
Richard Post, Isabel van den Heuvel, Marko Petkovic +1
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning (ML) methods can be used to study complex form…
Clustering of longitudinal data: A tutorial on a variety of approaches
Niek Den Teuling, Steffen Pauws, Edwin van den Heuvel
During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research. To support…
Individual causal effects from observational longitudinal studies with time-varying exposures
Richard Post, Zhuozhao Zhan, Edwin van den Heuvel
Causal effects may vary among individuals and can even be of opposite signs. When significant effect heterogeneity exists, the population average causal effect might be uninformati…
D-optimal designs for the Mitscherlich non-linear regression function
Maliheh Heidari, Md Abu Manju, Pieta C. IJzerman-Boon +1
Mitscherlich's function is a well-known three-parameter non-linear regression function that quantifies the relation between a stimulus or a time variable and a response. Optimal de…
Random autoregressive models: A structured overview
Marta Regis, Paulo Serra, Edwin R. van den Heuvel
Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The avail…
Simulation Models for Aggregated Data Meta-Analysis: Evaluation of Pooling Effect Sizes and Publication Biases
Edwin R. van den Heuvel, Osama Almalik, Zhuozhao Zhan
Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, r…
Joint modeling with time-dependent treatment and heteroskedasticity: Bayesian analysis with application to the Framingham Heart Study
Sandra Keizer, Zhuozhao Zhan, Vasan S. Ramachandran +1
Medical studies for chronic disease are often interested in the relation between longitudinal risk factor profiles and individuals' later life disease outcomes. These profiles may…
Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease
Abdolreza Mohammadi, Fentaw Abegaz, Edwin van den Heuvel +1
Dupuytren disease is a fibroproliferative disorder with unknown etiology that often progresses and eventually can cause permanent contractures of the affected fingers. In this pape…