Further updates in vignettes.

vignettes/Simulation_Example.Rmd

@@ -30,7 +30,7 @@ As in [link other vignette] we focus on the indication MDD and make use of histo
 More specifically trial results for BRINTELLIX will be utilized to establish an informative prior for the control group.
 
 # Calculation of a MAP prior
-In a first step a meta analytic prior will be calculated using historical data from 4 trials (with main endpoint CfB in MADRS score after 8 weeks).
+In a first step a meta analytic prior will be calculated using historical data from 5 trials (with main endpoint CfB in MADRS score after 8 weeks).
 Please note that only information from the control group will be integrated (leading to an informative multicomponent prior for the control group), while for the active groups a non-informative prior will be specified.
 
 
@@ -56,3 +56,60 @@ prior_list <- getPriorList(
 
 ```
 
+# Specification of new trial design 
+
+For the hypothetical new trial we plan with 4 active dose levels $2.5, 5, 10, 20$ and we specify a broad set of potential dose-response relationships, including a linear, an exponential, an emax and a sigEMax model.  
+Furthermore we assume a maximum effect of -3 on top of control (i.e. assuming that  active treatment can reduce the MADRS score after 8 weeks by up to 15.8) and plan a trial with 80 patients for all active groups and 60 patients for control.
+```{r}
+#Pre-Specification (B)MCPMod 
+
+## candidate models for MCPMod
+# linear function - no guestimates needed
+exp     <- DoseFinding::guesst(d     = 5,
+                               p     = c(0.2),
+                               model = "exponential",
+                               Maxd  = max(dose_levels))
+emax    <- DoseFinding::guesst(d     = 2.5,
+                               p     = c(0.9),
+                               model = "emax")
+sigemax<-  DoseFinding::guesst(d     = c(2.5,5),
+                               p     = c(0.1,0.6),
+                               model = "sigEmax")
+#beta <- DoseFinding::guesst(d=5, p=0.8, model="betaMod", dMax=1, scal=1.2, Maxd=20)
+
+mods <- DoseFinding::Mods(
+  linear      = NULL,
+  emax        = emax,
+  exponential = exp,
+  sigEmax     = sigemax,
+  #betaMod     = beta,
+  doses       = dose_levels,
+  maxEff      = -3,
+  placEff     = -12.8)
+
+n_patients=c(60,80,80,80,80)
+```
+
+# Calculation of success probabilities
+
+To calculate success probabilities for the different assumed dose-response models and the specified trial design we will apply the assessDesign function.  
+
+```{r}
+success_probabilities<-assessDesign (n_patients=n_patients,
+  mods=mods,
+  prior_list=prior_list)
+success_probabilities
+```
+
+As alternative design we will evaluate a design with the same overall sample size but putting more patients on the highest dose group (and control).
+
+```{r}
+success_probabilities_uneq<-assessDesign (n_patients=c(80,60,60,60,120),
+  mods=mods,
+  prior_list=prior_list)
+success_probabilities_uneq
+```
+
+For this specific trial setting the adapted allocation ratio leads to increased success probabilities under all assumed dose response relationships.
+
+

vignettes/analysis_normal.Rmd

@@ -25,7 +25,7 @@ set.seed(7015)
 # Background and data
 
 In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. 
-Hereby the focus is on the utilization of an informative prior.
+Hereby the focus is on the utilization of an informative prior and the BayesianMCPMod evaluation of a single trial.
 We will use data that is included in the clinDR package. 
 More specifically trial results for BRINTELLIX will be used to illustrate the specification of an informative prior and the usage of such a prior for the bayesian evaluation of a (hypothetical) new trial. 
 More information around BRINTELLIX to be added...