Expand vignette

Author: NightlordTW

vignettes/references.bib

@@ -110,4 +110,18 @@ @article{shieh_assessing_2022
   urldate = {2024-05-26}
 }
 
+@article{shin_randomized_2015,
+  title = {A {{Randomized}}, {{Phase I Pharmacokinetic Study Comparing SB2}} and {{Infliximab Reference Product}} ({{Remicade}}{\textregistered}) in {{Healthy Subjects}}},
+  author = {Shin, Donghoon and Kim, Youngdoe and Kim, Yoo Seok and K{\"o}rnicke, Thomas and Fuhr, Rainard},
+  year = {2015},
+  month = dec,
+  journal = {BioDrugs},
+  volume = {29},
+  number = {6},
+  pages = {381--388},
+  issn = {1173-8804, 1179-190X},
+  doi = {10.1007/s40259-015-0150-5}
+}
+
+
 

vignettes/sampleSize_parallel_3A2E.Rmd

@@ -37,19 +37,33 @@ When multiple primary endpoints are involved, a decision must be made on the des
 
 This vignette presents advanced techniques for calculating sample size in parallel trial designs involving three treatment arms and two endpoints. Specifically, it focuses on bioequivalence testing between a new treatment (SB2) and a reference drug (Remicade) administered in two distinct locations (EU_Remicade and USA_Remicade).
 
+As an illustrative example, we consider published data from the phase-1 trial [NCT01922336](https://clinicaltrials.gov/study/NCT01922336#study-overview). This trial assessed the pharmacokinetics of SB2 compared to its EU-sourced reference product. The following outcomes were reported following a single dose of SB2 or its EU reference product [@shin_randomized_2015]:
+
+```{r, echo=FALSE}
+data <- data.frame("PK measure" = c("AUCinf ($\\mu$g*h/mL)","AUClast ($\\mu$g*h/mL)","Cmax ($\\mu$g/mL)"),
+                   "SB2" = c("38,703 $\\pm$ 11,114", "36,862 $\\pm$ 9133", "127.0 $\\pm$ 16.9"), 
+                   "EU-INF" = c("39,360  $\\pm$ 12,332", "37,022 $\\pm$ 9398", "126.2 $\\pm$ 17.9"))
+
+kableExtra::kable_styling(kableExtra::kable(data, 
+                                            col.names = c("PK measure", "SB2", "EU-INF"),
+                                            caption = "Primary PK measures between test and reference product. Data represent arithmetic mean +- standard deviation."),
+                          bootstrap_options = "striped")
+```
+
+
 # Methodology and Assumptions
 
-The endpoints of interest are:
+The bioequivalence analysis focuses on two key pharmacokinetic endpoints:
 
 * AUCinf: Area Under the Curve (infinity)
 * Cmax: Maximum concentration
 
 For both endpoints, the analysis assumes that:
 
-* Summary data are available on the original scale (e.g., mean and standard deviation).
+* Summary data (e.g., mean and standard deviation) are available on the original scale.
 * These data are provided for each treatment arm.
 
-This vignette demonstrates how to compute the required sample size for testing bioequivalence, ensuring robust conclusions across endpoints in this three-arm parallel trial design.
+To evaluate bioequivalence, we apply the $80\%/125\%$ rule, which defines equivalence bounds relative to the reference mean. The evaluation is conducted using a one-sided significance level of 5\%, with a target statistical power of 90\%.
 
 # Testing the Difference of Means (DOM) for multiple Co-primary Endpoints
 In biosimilar development, it is important to demonstrate equivalence across all relevant doses, routes of administration, patient populations, and endpoints. To establish equivalence between two treatments, the difference in means for each endpoint, $\mu_{T}^{(j)} - \mu_{R}^{(j)}$, must lie within a predefined equivalence margin around zero for all primary endpoints.