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 

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. R^{2 }statistics) 
Robust methods 
Discrimination (cindex) 
Robust methods 
Prognostic Separation D statistic 
Rubin's rules 
Calibration (Shrinkage estimate) 
Robust methods 


Prediction 

Survival probabilities 
Rubin's rules after complementary loglog transformation 
Percentiles of a survival distribution 
Rubin's rules after logarithmic transformation 


* Reflect the authors' experiences and current evidence. 

Marshall et al. BMC Medical Research Methodology 2009 9:57 doi:10.1186/14712288957 