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Related Concept Videos

Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

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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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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Introduction to Nonparametric Statistics01:28

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Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
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Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models.

Emmanuelle Comets1, Christelle Rodrigues2, Vincent Jullien3

  • 1Universit́e de Paris, INSERM IAME; INSERM, CIC 1414; Rennes-1 University, France 16 rue Henri Huchard, 75018, Paris, France. emmanuelle.comets@inserm.fr.

Pharmaceutical Research
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

A new conditional bootstrap method improves uncertainty propagation in non-linear mixed effect models. This approach offers better parameter coverage than traditional methods, especially for variance parameters.

Keywords:
bootstrapconditional distributionnon-linear mixed effect modelsuncertainty of parameter estimates

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Area of Science:

  • Statistical modeling
  • Pharmacometrics
  • Computational statistics

Background:

  • Non-linear mixed effect (NLME) models are crucial for decision-making.
  • Accurate uncertainty propagation is essential for NLME model reliability.
  • Standard errors are often estimated using asymptotic methods, which may have limitations.

Purpose of the Study:

  • To propose a modified residual parametric bootstrap for NLME models.
  • To account for multiple levels of variability inherent in NLME models.
  • To evaluate the performance of the proposed bootstrap method.

Main Methods:

  • Implementation of a modified residual parametric bootstrap using R and the saemix algorithm.
  • Simulation studies comparing the proposed method with asymptotic approximation and standard bootstraps.
  • Assessment of coverage rates, parameter bias, and standard error bias.

Main Results:

  • The modified bootstrap demonstrated similar coverage to parametric bootstrap but with fewer assumptions.
  • Bootstrap methods generally improved coverage compared to asymptotic approximations, particularly for variance parameters.
  • All bootstrap methods showed sensitivity to estimation bias in the original datasets.

Conclusions:

  • The conditional bootstrap offers superior coverage rates compared to traditional residual bootstrap.
  • The proposed method effectively preserves the data-generating process structure.
  • This enhanced bootstrap approach improves uncertainty quantification in NLME models.