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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Missing data present challenges in clinical trial analysis.
  • Current research offers new guidance for preventing and treating missing data.
  • Defining clear objectives and causal estimands is crucial for robust trial design.

Purpose of the Study:

  • To provide guidance on preventing and treating missing data in clinical trials.
  • To discuss the selection of appropriate estimands (de facto vs. de jure) based on study objectives and stakeholder needs.
  • To illustrate the impact of adherence, rescue medication, and multiple estimands on trial results.

Main Methods:

  • Review of current research and consensus on missing data handling.
  • Illustrative examples demonstrating the application of different estimands and data inclusion strategies.
  • Discussion of implications for sample size and total exposure.

Main Results:

  • No single estimand is universally suitable; multiple estimands may be necessary.
  • Maximizing adherence can reduce sensitivity to missing data but may impact generalizability.
  • Inclusion of post-rescue data in primary analysis depends on the estimand and clinical context.

Conclusions:

  • Stakeholder input is vital for selecting estimands aligned with trial objectives.
  • Careful consideration of estimands, adherence, and data inclusion is essential for valid clinical trial interpretation.
  • Best practices for missing data management enhance the reliability of drug effectiveness and efficacy findings.