We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models.
Finally, we checked systematic reviews and validation studies of prediction models to identify other relevant articles for our validation study.
We opted to exclude participants with missing data on all other predictors, given the challenges of applying advanced imputation techniques for missing data in validation studies of risk models.
Costantino, J. P. et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence.
Other validation studies of the D&M model also used this conventional cut of point [ 13, 14].
In the present study, the best performing scoring system-based models showed AUCs between 0.79 and 0.80 (APACHE II, SAPS II) for predicting 6-month mortality, whereas external validation studies of the IMPACT model have shown AUCs up to 0.87 [ 47- 49].
We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.
Compared to other external validation studies of CKD risk models, we found the highest discrimination values [ 7].
Failure rates were displayed in a forest plot, together with the failure rates found in previous validation studies of the different models with D-dimer testing.
However, validation studies of U.S. EPA models have found them to reflect relative exposure fairly accurately (Payne-Sturges et al. 2004; State of New Jersey Department of Environmental Protection 2011; U.S. EPA Scientific Advisory Board 2001).
Recently, we published a validation study of the Simulation Model on Radiation Risk and breast cancer Screening (SiMRiSc), which quantified screening benefits and risks in various scenarios for women at an increased hereditary breast cancer risk (Greuter et al, 2010).
