Abstract Number:
2911
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Ahmad Hakeem Abdul Wahab (1)
Institutions:
(1) Janssen, N/A
First Author:
Presenting Author:
Abstract Text:
The ICH E9(R1) Addendum provides different strategies for addressing intercurrent events (ICEs: events post treatment initiation that may affect the interpretation or existence of clinical outcomes of interest) when defining an estimand and describing the targeted treatment effect (i.e., the estimand). The Principal Stratification (PS) strategy, mentioned in the ICH E9(R1) Addendum, is an appropriate approach to define a causal estimand, classifying subjects according to their potential occurrence of ICEs across treatment groups. Unfortunately, implementations of Principal Stratification have not proliferated the pharmaceutical scientific community. To resolve this, we introduce PrincipalR: An RShinyapp for Principal Stratification using varying models. Users can choose from Frequentist (multiple imputation method (Michael O'Kelly's implementation on Estimating Principal Strata in the DIA Missing Data Working Group), Principal Score Weighting (Ding & Lu, 2015), and Adherers Average Causal Effect (Qu et al., 2019)) to Bayesian (Wang et al., 2022) flavors, assess covariate distributions across observed ICEs and assess sensitivity of causal assumptions (Wang et al., 2022).
Keywords:
Causal Inference|Principal Stratification|Rshiny|Pharmaceutical|Intercurrent Events|ICH
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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