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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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Control-based imputation for sensitivity analyses in informative censoring for recurrent event data.

Fei Gao1, Guanghan F Liu2, Donglin Zeng1

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.

Pharmaceutical Statistics
|August 24, 2017
PubMed
Summary

This study introduces a new method for handling missing data in recurrent event clinical trials. The control-based multiple imputation technique improves the accuracy of results when participants stop adhering to study protocols.

Keywords:
bootstrapcontrol-based imputationmissing datamultiple imputationrecurrent event data

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Missing data are common in clinical trials, particularly due to nonadherence to treatment or study procedures.
  • Recurrent event endpoints are frequently analyzed using models that assume data are missing at random (MAR), an assumption untestable from observed data alone.
  • Sensitivity analyses are crucial for assessing the robustness of findings when MAR cannot be confirmed.

Purpose of the Study:

  • To implement and evaluate a control-based multiple imputation method for sensitivity analyses in recurrent event data.
  • To address the challenges posed by missing data in clinical trials with recurrent event outcomes.
  • To provide a robust analytical approach for situations with potential non-random missingness.

Main Methods:

  • Utilized control-based multiple imputation for sensitivity analyses of recurrent event data.
  • Modeled recurrent events using a piecewise exponential proportional intensity model incorporating frailty.
  • Sampled model parameters from the posterior distribution and imputed post-dropout events using a bootstrap procedure for variance correction.

Main Results:

  • The proposed method effectively imputes the number of events occurring after participant dropout.
  • Variance estimation is corrected using a bootstrap procedure, enhancing the reliability of the analysis.
  • The methodology was successfully applied to a clinical study involving sitagliptin.

Conclusions:

  • Control-based multiple imputation offers a valuable tool for sensitivity analyses in recurrent event clinical trials with missing data.
  • This approach enhances the validity of trial results when dealing with nonadherence and potential non-random missingness.
  • The method provides a more robust analysis framework compared to conventional models assuming MAR.