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Sensitivity analyses for informative censoring in survival data: A trial example.

Yanning Liu1,2

  • 1a Quantitative Science China, Janssen Research & Development , Shanghai , China.

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This study explores informative censoring in epilepsy drug trials. By imputing event times, it assesses how this impacts the log-rank test

Keywords:
Survival dataexpected time to eventinformative censoringrobustnesssensitivity

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Epilepsy Research

Background:

  • The log-rank test is standard for comparing survival curves in clinical trials, assuming non-informative censoring.
  • Violations of the proportional hazards assumption, often due to informative censoring, can compromise trial results.
  • Epilepsy drug trials frequently encounter challenges with patient adherence and withdrawal, leading to informative censoring.

Purpose of the Study:

  • To investigate the impact of informative censoring on time-to-event analysis in epilepsy clinical trials.
  • To develop and apply a method for imputing event times for subjects with informative censoring.
  • To assess the robustness of p-values derived from the log-rank test when informative censoring is present and handled through imputation.

Main Methods:

  • Utilized data from a randomized, double-blind, active-controlled epilepsy trial comparing two doses of study medication.
  • Developed a parametric approach to formulate and calculate the expected additional time to event for subjects with problematic informative censoring.
  • Imputed event times for censored subjects and re-calculated p-values using the log-rank test, comparing them to original un-imputed values. Kaplan-Meier plots were also compared.

Main Results:

  • The imputation method provided a way to handle informative censoring by estimating event times for withdrawn subjects.
  • Comparison of p-values from imputed versus un-imputed data demonstrated the potential impact of informative censoring on statistical significance.
  • Kaplan-Meier plots visually highlighted differences in survival curves when informative censoring was addressed.

Conclusions:

  • Informative censoring can significantly affect the validity of survival analyses in epilepsy trials.
  • The proposed imputation method offers a robust approach to assess the impact of informative censoring and maintain the integrity of p-values.
  • This methodology enhances the reliability of time-to-event analyses in clinical trials where non-informative censoring cannot be guaranteed.