diff --git a/vignettes/htrfit.Rmd b/vignettes/htrfit.Rmd
index cc567c0a50df3b2e6d0ac7875517aa5ab6472568..b376ffac4126e99d1b8535da066abb1797b03e60 100644
--- a/vignettes/htrfit.Rmd
+++ b/vignettes/htrfit.Rmd
@@ -583,56 +583,6 @@ resSimu <- evaluation_report(list_tmb = l_tmb,
 resSimu$performances
 ```
 
-## Evaluate model inference involving mixed effects
-
-For certain experimental scenarios, such as those involving a high number of levels or longitudinal data, the utilization of mixed effects within your design formula can be beneficial. The **HTRfit** simulation framework also offers the capability to assess this type of design formula.
-
-
-```{r example-evalMixed, warning = FALSE, message = FALSE}
-## -- init a design with a high number of levels
-input_var_list <- init_variable( name = "varA", mu = 0.2, sd = 0.74, level = 60) %>%
-                  init_variable( name = "varB", mu = 0.27, sd = 0.6, level = 2) %>%
-                    add_interaction( between_var = c("varA", "varB"), mu = 0.44, sd = 0.89)
-## -- simulate RNAseq data 
-mock_data <- mock_rnaseq(input_var_list, 
-                         n_genes = 30, ## small number to prevent excessively lengthy vignette construction
-                         min_replicates  = 10,
-                         max_replicates = 10, 
-                         basal_expression = 5 )
-## -- prepare data & fit a model with mixed effect
-data2fit = prepareData2fit(countMatrix = mock_data$counts, 
-                           metadata =  mock_data$metadata, 
-                           normalization = F,
-                           response_name = "kij")
-l_tmb <- fitModelParallel(formula = kij ~ varB + (varB | varA),
-                          data = data2fit, 
-                          group_by = "geneID",
-                          family = glmmTMB::nbinom2(link = "log"), 
-                          n.cores = 1)
-## -- evaluation
-resSimu <- evaluation_report(list_tmb = l_tmb,
-                             dds = NULL,
-                             mock_obj = mock_data,
-                             coeff_threshold = 0.27, 
-                             alt_hypothesis = "greater")
-```
-
-```{r example-outputResSimuMixed_id, warning = FALSE, message = FALSE, fig.align = 'center', fig.height = 4, fig.width = 5}
-## -- identity plot 
-###### 1) Model params
-resSimu$identity$params
-###### Dispersion params
-resSimu$identity$dispersion
-```
-
-```{r example-outputResSimuMixed_metric, warning = FALSE, message = FALSE, fig.align = 'center', fig.height = 4, fig.width = 7}
-## -- precision-recall curve
-resSimu$precision_recall$params
-## -- ROC curve
-resSimu$roc$params
-## -- Performances metrics
-resSimu$performances
-```
 
 ## Structure of evaluation report object