Clinical trial: Follow-up of control and treatment groups

This is an example of an experiment in which a treatment group is measured before and after the treatment. Also, a control group is measured before and after the placebo treatment. This design will allow estiamting the effect of a placebo and estimating the treatment effect after considering the effect of the placebo.
Controls
Treatment


Data will be generated according to the parameters selected. You can fix the mean and standard deviation in before and after the intervention for both the control (that is treated by a placebo) and the treatment group (that gets the treatment to evaluate)

Comparing the initial conditions in the control and treatment groups

We evaluate if the data in control and treatment groups are comparable in means at the initial conditions. In R:

t.test(Initial~Group,data=df) 


              

                res <- t.test(Initial~Group,data=df)
              

              

                round(res$conf.int,2)
              

              

            

            

              
Estimation of the placebo effect

              The placebo effect can be chracterized by estimating the mean of the
                                                             difference between final and initial values in the control group. In R:
              

                df <- df %>% filter(Group=='Control')
              

              

                with(df,t.test(dif))
              

              

              

                df <- df %>% filter(Group=='Control')
              

              

                res <- with(df,t.test(dif))
              

              

                round(res$conf.int,2)
              

              

            

            

              
Evolution of the treatment group

              The treatment effect can be chracterized by estimating the mean of the
                                                             difference between final and initial values in the treatment group. In R:
              

                df <- df %>% filter(Group=='Trata')
              

              

                with(df,t.test(dif))
              

              

              

                df <- df %>% filter(Group=='Trata')
              

              

                res <- with(df,t.test(dif))
              

              

                round(res$conf.int,2)
              

              

            

            

              
Estimation of the treatment effect

              The treatment effect can be chracterized by estimating the difference of means 
                                                          between the observed evolution (dif) in both the control and treatment groups. In R:
              

                t.test(dif~Group,data=df)
              

              

              

                res <- t.test(dif~Group,data=df)
              

              

                round(res$conf.int,2)