Table 1 Parameter of interest in prognostic modelling studies and ways to combine estimates after MI
Parameters | Possible methods for combining estimates of parameters after MI* |
|---|---|
Covariate distribution | Â |
Mean Value | Rubin's rules |
Standard Deviation | Rubin's rules |
Correlation | Rubin's rules after Fisher's Z transformation |
Model parameters | Â |
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 | Â |
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 | Â |
Survival probabilities | Rubin's rules after complementary log-log transformation |
Percentiles of a survival distribution | Rubin's rules after logarithmic transformation |