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Related Concept Videos

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Friedman Two-way Analysis of Variance by Ranks01:21

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Two-Way ANOVA01:17

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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Updated: Dec 21, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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An iterative algorithm for joint covariate and random effect selection in mixed effects models.

Maud Delattre1, Marie-Anne Poursat2

  • 1UMR MIA-Paris, AgroParisTech, INRAE, Université Paris-Saclay, 75005, Paris, France.

The International Journal of Biostatistics
|May 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new stepwise algorithm for simultaneously selecting fixed and random effects in mixed-effects models, improving model interpretation and accuracy in clinical studies.

Keywords:
bayesian information criterionjoint covariate and random effects selectionnonlinear mixed effects modelsstepwise procedure

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Interpreting mixed-effects models is complex due to the interdependence of fixed and random effects.
  • Simultaneous selection of these effects is crucial for accurate model building.

Purpose of the Study:

  • To develop and evaluate a novel stepwise algorithm for joint selection of fixed and random effects in mixed-effects models.
  • To enhance the interpretability and accuracy of statistical models in various settings.

Main Methods:

  • A stepwise selection algorithm based on adapted Bayesian Information Criteria (BIC) for mixed-effects models.
  • The algorithm performs simultaneous selection of fixed and random effects in both linear and nonlinear models.
  • Applicable in low-dimension settings with moderate numbers of covariates and random effects relative to observations.

Main Results:

  • The proposed algorithm demonstrates effective simultaneous selection of fixed and random effects.
  • Simulation studies confirm the algorithm's performance, outperforming existing alternatives where applicable.
  • The method's utility is illustrated through a clinical study on antibiotic agent kinetics.

Conclusions:

  • The developed stepwise algorithm provides a robust method for joint fixed and random effects selection.
  • This approach enhances the reliability of mixed-effects model interpretation and application.
  • The method is particularly valuable for complex clinical and biological data analysis.