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Table 1 Parameter of interest in prognostic modelling studies and ways to combine estimates after MI

From: Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Parameters Possible methods for combining estimates of parameters after MI*
Covariate distribution  
Mean Value Rubin's rules
Standard Deviation Rubin's rules
Correlation Rubin's rules after Fisher's Z transformation
Model parameters  
Regression coefficient Rubin's rules
Hazard ratio Rubin's rules after logarithmic transformation
Prognostic Index/linear predictor per patient Rubin's rules
Model fit and performance  
Testing significance of individual covariate in model Rubin's rules using a Wald test for a single estimates (Table 2(A))
Testing significance of all fitted covariates in model Rubin's rules using a Wald test for multivariate estimates (Table 2(B))
Likelihood ratio χ 2 test statistic Rules for combining likelihood ratio statistics if parametric model (Table 2(D)) or χ 2 statistics if Cox model (Table 2(C))
Proportion of variance explained (e.g. R2 statistics) Robust methods
Discrimination (c-index) Robust methods
Prognostic Separation D statistic Rubin's rules
Calibration (Shrinkage estimate) Robust methods
Prediction  
Survival probabilities Rubin's rules after complementary log-log transformation
Percentiles of a survival distribution Rubin's rules after logarithmic transformation
  1. * Reflect the authors' experiences and current evidence.