Prediction models Sample Clauses

Prediction models. To develop the models, all the factors that significantly influenced the risk of CD devel- opment were combined into a risk score. Multivariable ▇▇▇ proportional hazards regression analysis of the baseline was per- formed in two steps. In the first step, three primary variables already known at the child’s birth (gender, HLA-risk group, number of affected FDR, table 1) were entered into the model, irrespective of statistical significance. In accordance with our previous publication, we analyzed the risk for CD in five groups according to HLA-DQ genotype (see Supplementary Appendix).5 In addition, we also exploratively analyzed the risk for CD in children with DR3-DQ2/DR3-DQ2 separately from those with DR3-DQ2/DR7-DQ2, as the affinity of gluten peptides is higher for DR3-DQ2 than for DR7-DQ2 receptors.7,8 Because of the low number of children with 3 or more affected FDRs (7), these were considered together in one category. The second step consisted of adding the secondary variables (country of origin, type of affected FDR, maternal diet, delivery mode and early intervention with gluten or placebo, table 1) to the model using backward elimination based on Akaike Information Criterion (AIC), thus guarding against overfitting.9,10 Analyses for variables occurring after birth (duration of breastfeeding, duration of exclu- sive breastfeeding, rotavirus vaccination, infections as reported by parents and gluten intake) were performed at one, two and three years of age (infections until six years of age) (Supplementary Appendix). For each analysis, the information available at the landmark time point was used. Models’ backward elimination based on AIC was used. Since quantification of daily gluten intake is usually unknown in the standard medi-

Prediction models. The goal is to develop methods for screening/predicting patients at high risk of developing cancer by identification of predictors (risk factors) for developing cancer in patients with diabetes mellitus treated with insulins. Methods will be described by WP6.

Prediction models. Two models are proposed (i) an overall/general, and (ii) a resilience-trajectory specific.