Revise vignette

Author: Thomas Debray

vignettes/intropkg.Rmd

@@ -115,7 +115,7 @@ One possible solution, if the required sample size is not feasible, is to power
 
 ## Testing multiple primary endpoints
 
-When a trial aims to evaluate equivalence for at least $k$ out of $m$ primary endpoints, adjustment for the increased probability of a Type I error due to multiple hypothesis testing is required. For example, with $m=3$ independent primary endpoints and a significance level of $\alpha = 5\%$, the probability of making a Type I error on at least one hypothesis test is:
+When a trial aims to evaluate equivalence for at least $k$ primary endpoints, adjustment for the increased probability of a Type I error due to multiple hypothesis testing is required [@sozu_continuous_2015]. For example, with $m=3$ independent primary endpoints and a significance level of $\alpha = 5\%$, the probability of making a Type I error on at least one hypothesis test is:
 
 $$ 1 – (1-\alpha)^m  = 1 - (1-0.05)^3 = 0.1426. $$
 This means that the overall probability of making any false positive error, also known as the **family-wise error rate (FWER)**, increases to approximately 14%. 

vignettes/references.bib

@@ -135,6 +135,21 @@ @book{sozu_sample_2015
   isbn = {978-3-319-22004-8 978-3-319-22005-5}
 }
 
+@inbook{sozu_continuous_2015,
+  title        = {Continuous Primary Endpoints},
+  booktitle    = {Sample Size Determination in Clinical Trials with Multiple Endpoints},
+  author       = {Sozu, Takashi and Sugimoto, Tomoyuki and Hamasaki, Toshimitsu and Evans, Scott R.},
+  year         = {2015},
+  pages        = {59--68},
+  publisher    = {Springer International Publishing},
+  address      = {Cham},
+  doi          = {10.1007/978-3-319-22005-5_5},
+  urldate      = {2025-02-10},
+  isbn         = {978-3-319-22004-8, 978-3-319-22005-5},
+  langid       = {english}
+}
+
+
 @incollection{PASSch351,
   title        = {Bioequivalence Tests for AUC and Cmax in a 2x2 Cross-Over Design (Log-Normal Data)},
   author       = {{NCSS}},