It is a macro which allows to rapidly validate a pronostic model thanks to discirmination (AUC and ROC curve), calibration (Hosmer-Lemeshow test and graph) and summarize quality of the model at the end. You just must have probability of outcome and the outcome in a table. This macro is very interesting for a validation dataset of a logistic model. Adrien FRANCAIS

It is a macro which allows to find risk factor of outcome for qualitative variables. You can test binary or classes variables. A Khi2 test for each modality is available. Results are presented with frequencies, percentages, Khi2 test and missing values. An example will help you Adrien FRANCAIS and Aurélien VESIN

It is a macro which allows to transform quantitative variables in classes. You can choose the number of classes (2,4,5,10...)and the type of transformation : several binary variables according the percentile or only one new variable divided in 'n' classes. An example will help you. Adrien FRANCAIS and Aurélien VESIN

It is a macro which allows to transform qualitative variables in binary variables. An example will help you. Adrien FRANCAIS

Macro which realizes a N:M matching according one or several qualitative variables Designed by Aurélien VESIN

Designed by Muriel TAFFLET Modified by Adrien FRANCAIS 

It is a macro which allows to find imbalances between groups for quantitative variables according to an outcome binary variable. You have just to put the list of variables to test and you can choose the appearance of results (complete or simplified presentation). An example will help you. Adrien FRANCAIS

Designed by Muriel TAFFLET OUTCOMEREA biostatistical department France

Macro to describe quantitatives variables in a cohort (median, mean, quartiles... and missing values)   Edited by Adrien FRANCAIS adrien.francais@ujf-grenoble.fr    

Macro to describe cohort with only frequencies, percentages and missing values for each modality of qualitative variables   Edited by Adrien FRANCAIS adrien.francais@ujf-grenoble.fr  

It is a macro which allows to deconcatenate variable which contain multiple values separated by a character. For example, if a variable is '674|675|676|677', we then create 4 new variables : 674 for the first, 675 for the second...until the last. An example will help you. Adrien FRANCAIS and Valérie SIROUX

It is a macro which allows to decompose variables created in additional codes. We then obtain several variables with unique code to clearly identify value. For example, if a variable is 19, we create 3 new variables with the code 16 for the first, 2 for the second and 1 for the last. An example will help you. Adrien FRANCAIS

Macro to compare two groups according to a binary variable. Effectives and Frequencies are computed for qualitative variables. Distribution is described for quantitative variables. Differences between groups is computed thanks to Khi2 test and Kruskall-Wallis test. If the analyse is stratified, a term is available for that and pvalue appropriate is computed in logistic conditional regression. Edited by Adrien FRANCAIS adrien.francais@ujf-grenoble.fr

Macro to compare a quantitative variable to subgroups   Edited by Adrien FRANCAIS adrien.francais@ujf-grenoble.fr

It is a macro which allows to select automatically fixed effects in PROC GLIMMIX. You can choose the type of selection (BACKWARD or FORWARD), the threshold, until 5 level of random effects and force a variable in the model. In order to algorithm converge, datafile must have not a lot of missing values Adrien FRANCAIS