Table 4

Selection of variables in multivariable analysis

% (n = 43*)


Selection of variables for inclusion in multivariable analysis

All candidate variables used (no selection)

26 (11)

All candidate variables apart from a few with contra indications**

5 (2)

Without statistical analysis

Previous literature

5 (2)

Previous literature and few variables by investigator choice

5 (2)

By statistical analysis

Screening by univariable analysis - only significant variables

37 (16)

Screening by univariable analysis - significant variables and investigator choice

11 (5)

Unclear/Not reported

11 (5)


Statistical modelling methods used within multivariable analysis

A priori variables fixed, others added

2 (1)

Backward elimination

14 (6)

Forward selection

5 (2)

Other (pairwise multiple testing for categories of variables)

2 (1)

Unclear/Not reported

77 (33)


Methods for inclusion of variables in final model and prognostic index

No selection. All variables kept in model

14 (6)

Retain only significant variables based on P-value

65 (28)

Retain significant variables plus variables based on previous literature

2 (1)

Retain all variables but alter prognostic score after model to include only significant variables and adjust for other variables

5 (2)

Retain only significant variables but alter prognostic score after final model

5 (2)

Retain based on model goodness of fit

2 (1)

Unclear/Not reported

7 (3)


* Excluded four studies using recursive partitioning analysis and artificial neural network models

** Contra indications reported as reasons for exclusion of variables were missing data, collinearity and treatment indicator

Mallett et al. BMC Medicine 2010 8:20   doi:10.1186/1741-7015-8-20

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