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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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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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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 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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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

Mathyn Vervaart1, Eline Aas1,2, Karl P Claxton3,4

  • 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
|March 27, 2023
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New methods for simulating survival data reduce computational burden for expected value of sample information (EVSI) calculations, especially when accounting for treatment effect waning or using flexible survival models.

Keywords:
economic evaluation modelexpected value of sample informationsimulation methodssurvival datavalue of information

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Area of Science:

  • Decision analysis
  • Biostatistics
  • Health economics

Background:

  • Expected value of sample information (EVSI) quantifies the value of additional data to reduce decision-making uncertainty.
  • Standard EVSI calculations often rely on inverse transform sampling (ITS), which is computationally intensive for complex survival models.
  • Challenges arise with flexible survival models or when assuming treatment effect waning, where closed-form quantile functions are unavailable.

Purpose of the Study:

  • To develop and present general-purpose methods for simulating survival data.
  • To standardize and reduce the computational burden of the EVSI data-simulation step.
  • To address EVSI calculations for survival data with treatment effect waning or flexible models.

Main Methods:

  • Developed a discrete sampling method for simulating survival data.
  • Developed an interpolated inverse transform sampling (ITS) method.
  • Compared these methods against standard ITS using a partitioned survival model, with and without treatment effect waning.

Main Results:

  • The discrete sampling and interpolated ITS methods closely align with the standard ITS method.
  • These new methods significantly reduce computational cost, particularly when adjusting for treatment effect waning.
  • The developed methods demonstrated high accuracy in EVSI estimation.

Conclusions:

  • Presented general-purpose methods for simulating survival data that reduce computational burden for EVSI calculations.
  • The methods are applicable to survival models with treatment effect waning and flexible survival models.
  • Implementation is standardized across survival models and easily automated for probabilistic decision analyses.