In pharmaceutical drug development, it is important to identify the optimal dose for confirmatory trials. A typical development strategy is to design a phase 2 dose-ranging study with several doses. Based on the benefit–risk observed from the dose-ranging study, an optimal dose may be selected for phase 3 confirmatory trials. To accelerate clinical development, a seamless 2/3 adaptive design is an attractive strategy to combine phase 2 dose selection with phase 3 confirmatory objectives. In an inferential seamless phase 2/3 design, the phase 3 dose is usually selected based on phase 2 results and carried forward in phase 3. In addition, data obtained from both phases will be combined in the final analysis. In many disease settings, biomarker or short-term efficacy endpoints may be utilized for dose selection as they are correlated with the confirmatory clinical efficacy endpoints. As discussed in Li et al. (Multiplicity adjustment in seamless phase II/III adaptive trials using biomarkers for dose selection. In Applied Statistics in Biomedicine and Clinical Trials Design. ICSA Book Series in Statistics, pp. 285–299. Springer International Publishing Switzerland (2015)), the combined analysis may cause type I error inflation due to the correlation and dose selection. Sidák adjustment has been proposed to control the overall type I error by adjusting p-values in phase 2 when performing the combined p-value test. However, this adjustment could be overly conservative as it does not consider the underlying correlations among doses/endpoints. Wang et al. (Contemporary Clinical Trials, 132, 107300 (2023)) propose an alternative approach utilizing biomarker rank-based ordered test statistics, which takes the rank order of the selected dose and the correlation into consideration. When the correlation is unknown, a rank-based Dunnett adjustment is utilized, which includes the traditional Dunnett adjustment (Dunnett, Journal of the American Statistical Association, 75, 796–800 (1980)) as a special case. The rank-based method controls the overall type I error and leads to a uniformly higher power than Sidák adjustment and the traditional Dunnett adjustment under all potential correlation scenarios discussed. In this extension, the authors generalize the correlation matrix to be even more flexible in sample sizes, introduce an R-shiny App to facilitate simulations, and discuss results and simulations for other common dose-finding designs including 2, 4, and 5 doses vs. a common control.