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Table 4 Selection of variables in multivariable analysis

From: Reporting methods in studies developing prognostic models in cancer: a review

  % (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)
  1. * Excluded four studies using recursive partitioning analysis and artificial neural network models
  2. ** Contra indications reported as reasons for exclusion of variables were missing data, collinearity and treatment indicator