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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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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 curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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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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An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.

Mathyn Vervaart1,2, Mark Strong3, Karl P Claxton4,5

  • 1Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|December 30, 2021
PubMed
Summary
This summary is machine-generated.

We developed a fast, straightforward regression method to calculate the expected value of sample information (EVSI) for extending health technology trials. This helps determine if more data is needed before making adoption decisions.

Keywords:
Monte Carlo methodsbayesian decision theorycomputational methodseconomic evaluation modelexpected value of sample informationgeneralized additive modelmodel averagingnonparametric regressionsurvival data

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Area of Science:

  • Health economics
  • Decision analysis
  • Biostatistics

Background:

  • Health technology decisions are often made with early-stage trial data, leading to uncertainty in key outcomes like life expectancy.
  • Collecting additional data can reduce uncertainty, and its value is quantified by the expected value of sample information (EVSI).
  • EVSI is typically used for designing future trials, not for ongoing ones.

Purpose of the Study:

  • To develop and present new methods for computing the EVSI of extending an existing trial's follow-up.
  • To address both single-model assumptions and model uncertainty in survival analysis.
  • To provide tools for decision-makers regarding ongoing health technology trials.

Main Methods:

  • Developed a nested Markov Chain Monte Carlo (MCMC) procedure.
  • Developed a nonparametric regression-based method.
  • Compared these methods using single-model and model-averaged EVSI in synthetic case studies.

Main Results:

  • The regression-based method showed good agreement with the MCMC procedure.
  • The regression method was fast, easy to implement, and scalable for multiple survival models.
  • The MCMC procedure was computationally intensive, especially with model uncertainty.

Conclusions:

  • A straightforward regression-based method for computing EVSI of extended trial follow-up is presented.
  • This method handles both single-model and model-uncertainty scenarios.
  • EVSI for ongoing trials aids decisions on early patient access versus needing more mature evidence.