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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Sample size calculation for the proportional hazards cure model.

Songfeng Wang1, Jiajia Zhang, Wenbin Lu

  • 1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA. songfeng@gmail.com

Statistics in Medicine
|July 13, 2012
PubMed
Summary

Designing clinical trials with potential cures requires accounting for cure fractions. This study develops a new sample size formula based on the proportional hazards cure model, improving accuracy for survival trials.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Clinical trials with time-to-event endpoints often include patients who are cured or long-term survivors.
  • The proportional hazards (PH) model, commonly used for sample size calculations, may be inappropriate when a cure fraction exists, as its assumptions can be violated.
  • The PH cure model, which incorporates a logistic distribution for cure probability, is a practical approach for such scenarios.

Purpose of the Study:

  • To develop a sample size formula for survival trials that accounts for a cure fraction, using the PH cure model.
  • To provide a more flexible formula capable of testing differences in short-term survival and/or cure rates.
  • To assess the impact of trial design parameters on sample size calculations.

Main Methods:

  • Developed a sample size formula based on the asymptotic distributions of weighted log-rank statistics under null and local alternative hypotheses within the PH cure model.
  • Investigated the influence of accrual methods, accrual duration, and follow-up periods on sample size.
  • Evaluated the proposed formula's performance through simulation studies.

Main Results:

  • Ignoring the cure rate in sample size calculations can result in underpowered or overpowered studies.
  • The derived sample size formula under the PH cure model offers greater flexibility for analyzing survival data with a cure fraction.
  • Numerical examples demonstrated the impact of various trial design parameters on sample size.

Conclusions:

  • Accurate sample size calculation in survival trials with a cure fraction is crucial for study validity.
  • The proposed PH cure model-based sample size formula provides a more robust approach compared to traditional methods.
  • The findings highlight the importance of considering cure rates in the design of clinical trials for diseases with potential cures, such as non-Hodgkin's lymphoma.