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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Fast tipping point sensitivity analyses in clinical trials with missing continuous outcomes under multiple

Anders Gorst-Rasmussen1, Mads Jeppe Tarp-Johansen2

  • 1Biostatistics Centre of Expertise Novo Nordisk A/S, Denmark.

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|June 2, 2022
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Summary

This study introduces an efficient method for tipping point analysis in clinical trials with missing data. It simplifies assessing treatment effect robustness, even with complex missing data patterns.

Keywords:
Missing datamultiple imputationreplication

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Data Science

Background:

  • Missing data in clinical trials necessitates robust analysis methods.
  • Sensitivity analyses, like tipping point analysis, are crucial for regulatory submissions.
  • Existing two-way tipping point analyses for continuous outcomes under multiple imputation can be computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient method for two-way tipping point analysis.
  • To simplify the assessment of robustness to missing data assumptions in clinical trials.
  • To extend tipping point analysis to a multi-way setting with general conditions for robustness.

Main Methods:

  • Developed an efficient computational approach for two-way tipping point analysis.
  • Utilized geometric properties to further simplify the analysis of missing data impact.
  • Proposed a novel extension for multi-way tipping point analysis.

Main Results:

  • The proposed method significantly reduces computational burden in two-way tipping point analysis.
  • Geometric insights offer a streamlined way to explore missing data effects.
  • The multi-way extension provides simple, general conditions for assessing robustness.

Conclusions:

  • The new method enhances the efficiency and applicability of tipping point analysis.
  • This approach improves the reliability of conclusions drawn from clinical trial data with missing values.
  • The findings offer valuable tools for regulatory agencies and researchers evaluating treatment effects.