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  3. Using Off-treatment Sequential Multiple Imputation For Binary Outcomes To Address Intercurrent Events Handled By A Treatment Policy Strategy.
  1. Home
  3. Using Off-treatment Sequential Multiple Imputation For Binary Outcomes To Address Intercurrent Events Handled By A Treatment Policy Strategy.

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Using Off-Treatment Sequential Multiple Imputation for Binary Outcomes to Address Intercurrent Events Handled by a

Sunita Rehal1, Nicky Best1, Sarah Watts1

  • 1GlaxoSmithKline, London, UK.

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|February 7, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The estimand framework guides handling intercurrent events, favoring the treatment policy strategy. A simple retrieved dropout model is pragmatic for binary endpoints, but requires substantial post-event data for reliable estimation.

Keywords:
binary endpointestimandsmissing datamultiple imputationtreatment policy

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Pharmaceutical Research

Background:

  • The estimand framework offers strategies for intercurrent events in clinical trials.
  • The treatment policy strategy aligns with the intention-to-treat principle.
  • Handling missing data due to patient withdrawal is crucial for accurate estimation.

Purpose of the Study:

  • To explore and compare retrieved dropout models for estimating treatment effects with binary endpoints under a treatment policy strategy.
  • To assess the performance of these models via simulation and a practical example.
  • To identify the most pragmatic retrieved dropout model for handling intercurrent events like treatment discontinuation.

Main Methods:

  • Investigated various retrieved dropout models for imputing missing post-intercurrent event data.
  • Compared proposed models against a simple imputation model using a simulation study.
  • Applied and evaluated models using Phase 3 clinical trial data for rheumatoid arthritis.

    • Main Results:

    • A simple retrieved dropout model with an intercurrent event indicator proved most pragmatic.
    • The model's effectiveness is contingent on having at least 50% of observed post-intercurrent event data.
    • Model suitability depends on the availability of observed data post-intercurrent event.

    Conclusions:

    • Retrieved dropout models offer a viable approach for estimating treatment effects with binary outcomes and intercurrent events.
    • The pragmatic choice requires sufficient observed data post-intercurrent event for reliable imputation.
    • Careful consideration of data availability is essential before implementing these models in clinical trials.