126 citations · 129 across the 6 of their papers we have counts for
9 papers
Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation
Linda Nab, Rolf H. H. Groenwold
Sensitivity analysis for measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been com…
Identification of causal effects in case-control studies
Bas B. L. Penning de Vries, Rolf H. H. Groenwold
Case-control designs are an important tool in contrasting the effects of well-defined treatments. In this paper, we reconsider classical concepts, assumptions and principles and ex…
mecor: An R package for measurement error correction in linear regression models with a continuous outcome
Linda Nab, Maarten van Smeden, Ruth H. Keogh +1
Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated co…
Prediction meets causal inference: the role of treatment in clinical prediction models
Nan van Geloven, Sonja Swanson, Chava Ramspek +7
In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medi…
Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models
Linda Nab, Rolf H. H. Groenwold, Maarten van Smeden +1
In observational research treatment effects, the average treatment effect (ATE) estimator may be biased if a confounding variable is misclassified. We discuss the impact of classif…
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold
Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a n…
Measurement error in continuous endpoints in randomised trials: problems and solutions
Linda Nab, Rolf H. H. Groenwold, Paco M. J. Welsing +1
In randomised trials, continuous endpoints are often measured with some degree of error. This study explores the impact of ignoring measurement error, and proposes methods to impro…
Propensity score estimation using classification and regression trees in the presence of missing covariate data
Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold
Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity…
Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective
Kim Luijken, Rolf H. H. Groenwold, Ben van Calster +2
It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived.…