Table 1

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


Possible methods for combining estimates of parameters after MI*

Covariate distribution

Mean Value

Rubin's rules

Standard Deviation

Rubin's rules


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 χ2 test statistic

Rules for combining likelihood ratio statistics if parametric model (Table 2(D)) or χ2 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


Survival probabilities

Rubin's rules after complementary log-log 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:10.1186/1471-2288-9-57

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