The estimand framework, introduced in the ICH E9 (R1) Addendum, provides a structured approach for defining precise research questions in randomised clinical trials. It suggests five strategies for addressing intercurrent events (ICE). This case study examines the principal stratum strategy, highlighting its potential for estimating causal treatment effects in specific subpopulations and the challenges involved. The occurrence of anti-drug antibodies (ADAs) and their potential clinical impact are important factors in evaluating biosimilars. Typically, analyses focus on subgroups of patients who develop ADAs during the study. However, conducting subgroup analyses based on post-randomisation variables, such as immunogenicity, can introduce substantial bias into treatment effect estimates and is therefore methodologically not optimal. The principal stratum strategy provides a statistical pathway for estimating treatment effects in subpopulations that cannot be anticipated at baseline. By leveraging counterfactuals to assess treatment outcomes, with and without the incidence of intercurrent events (ICEs), this approach can be implemented through a missing data perspective. We demonstrate the implementation of the principal stratum strategy in a phase 3 equivalence trial of a biosimilar for the treatment of rheumatoid arthritis. Using a multiple imputation approach, we leverage longitudinal measurements to create analysis datasets for subpopulations who develop ADAs as ICE. Our results highlight the principal stratum strategy's potential and challenges, emphasising its reliance on unobserved ICE states and the need for complex and rigorous modelling. This study contributes to a nuanced understanding and practical implementation of the principal stratum strategy within the ICH E9 (R1) framework.