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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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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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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Sample size estimation for recurrent event data using multifrailty and multilevel survival models.

Derek Dinart1,2, Carine Bellera1,2, Virginie Rondeau1,3

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This summary is machine-generated.

Calculating sample size for recurrent events in clinical research is crucial. This study analyzes sample size for recurrent time-to-event data using frailty models, finding increased sample needs with higher heterogeneity.

Keywords:
Sample sizefrailty modeljoint modelsrandomizedrecurrent events

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Area of Science:

  • Epidemiology and clinical research
  • Biostatistics
  • Survival analysis

Background:

  • Recurrent events, such as hospitalizations or fractures, are common in clinical studies.
  • Accurate sample size estimation is vital for the statistical power of recurrent event analyses.
  • Existing methods for sample size calculation in recurrent event data analysis are varied.

Purpose of the Study:

  • To provide an in-depth analysis of sample size requirements for recurrent time-to-event data.
  • To compare different frailty models (shared, hierarchical, joint) for sample size estimation.
  • To offer guidance on optimizing study design for recurrent event studies.

Main Methods:

  • Utilized Wald-type test statistics for sample size estimation.
  • Investigated simple shared frailty models to complex multilevel survival models.
  • Conducted simulations to assess sample size under varying heterogeneity.
  • Applied the methodology to the AFFIRM-AHF trial data.

Main Results:

  • Sample size requirements increase with greater heterogeneity in recurrent event data.
  • Including more patients with shorter follow-up is generally more efficient than fewer patients with longer follow-up.
  • Different frailty models provide suitable sample size calculations depending on the research question.

Conclusions:

  • The choice of frailty model impacts sample size calculations for recurrent events.
  • Study design, balancing patient numbers and follow-up duration, is critical for achieving required event counts.
  • The presented methods offer a robust framework for sample size determination in recurrent event studies.