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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
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Weibull Distribution
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Published on: July 25, 2013

Selection models with monotone weight functions in meta analysis.

Kaspar Rufibach1

  • 1Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland. kaspar.rufibach@ifspm.uzh.ch

Biometrical Journal. Biometrische Zeitschrift
|May 14, 2011
PubMed
Summary
This summary is machine-generated.

Publication bias threatens meta-analysis validity. This study introduces a non-parametric method to model publication bias using a decreasing weight function, enhancing meta-analysis reliability.

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Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Publication bias, where selected studies don't represent all research, is a significant threat to meta-analysis validity.
  • Existing methods for modeling publication bias often rely on parametric weight functions, which may not capture the full complexity of the issue.

Purpose of the Study:

  • To develop and evaluate a non-parametric approach for modeling publication bias in meta-analysis.
  • To introduce a method that imposes a monotonically non-increasing weight function based on p-values.
  • To provide tools for estimating treatment effects adjusted for selection bias.

Main Methods:

  • Adoption of a non-parametric approach for modeling publication bias, building on Dear and Begg (1992).
  • Imposition of a monotonically non-increasing weight function dependent on p-values.
  • Estimation of a decreasing weight function and computation of a p-value to test for constant weight (i.e., no bias).
  • Development of an approximate selection bias adjusted profile likelihood confidence interval for treatment effects.

Main Results:

  • The proposed methodology allows for the estimation of a decreasing weight function, addressing limitations of existing parametric models.
  • A p-value is provided to quantify evidence against the null hypothesis of a constant weight function, indicating the presence of publication bias.
  • An approximate confidence interval for the treatment effect, adjusted for selection bias, is presented.

Conclusions:

  • The developed non-parametric method offers a robust approach to modeling and adjusting for publication bias in meta-analysis.
  • The methodology provides a quantitative measure to assess the evidence of publication bias.
  • The associated R package 'selectMeta' facilitates the application of these methods in practice.