Now showing items 1-6 of 6
A new approach to categorize continuous variables in prediction models: Proposal and validation
When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from ...
Sample size impact on the categorisation of continuous variables in clinical prediction
Recent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor ...
Correcting for spatial heterogeneity in plant breeding experiments with P-splines
An important aim of the analysis of agricultural field experiments is to obtain good predictions for genotypic performance, by correcting for spatial effects. In practice these corrections turn out to be complicated, since ...
Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model
Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process ...
Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies
The Cox proportional hazards model is the most widely used survival prediction model for analysing timeto-event data. To measure the discrimination ability of a survival model the concordance probability index is widely ...
Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis
Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for ...