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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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Missing data sensitivity analysis for recurrent event data using controlled imputation.

Oliver N Keene1, James H Roger, Benjamin F Hartley

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Summary
This summary is machine-generated.

This study introduces a novel statistical method for analyzing recurrent event data in clinical trials, enhancing accuracy when treatment adherence varies. The approach uses negative multinomial distribution for more robust treatment effect estimation.

Keywords:
MNARexacerbationmissingmultiple imputationrecurrent eventsensitivity

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Recurrent event data analysis traditionally assumes data are 'missing at random' (MAR).
  • This MAR assumption implies perfect treatment adherence, which may not reflect real-world clinical trial scenarios.
  • Confirmatory trials necessitate sensitivity analyses for 'de facto' estimands that account for non-adherence.

Purpose of the Study:

  • To extend reference-based imputation methods, previously used for continuous outcomes, to recurrent event data.
  • To apply the negative multinomial distribution for analyzing recurrent events using controlled imputation.
  • To provide a robust statistical framework for estimating treatment effects in the presence of non-adherence.

Main Methods:

  • Utilized the negative multinomial distribution for recurrent event data analysis.
  • Employed a reference-based imputation strategy, drawing from pattern mixture models.
  • Imputed missing data in one treatment arm based on the observed data from another arm (e.g., placebo arm).

Main Results:

  • Demonstrated the application of the negative multinomial distribution with controlled imputation for recurrent event data.
  • Illustrated the methodology using data from a severe asthma trial focused on exacerbation rates.
  • The negative binomial model served as the basis for the primary analysis, adapted for sensitivity analyses.

Conclusions:

  • The proposed method offers a valuable tool for sensitivity analyses in clinical trials with recurrent events.
  • Reference-based imputation using the negative multinomial distribution provides a more realistic estimation of treatment effects under non-adherence.
  • This approach enhances the reliability of statistical analyses for recurrent event data in clinical research.