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Related Concept Videos

Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

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Related Experiment Video

Updated: May 13, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Copas-Heckman-Type Sensitivity Analysis for Publication Bias in Rare-Event Meta-Analysis Under Generalized Linear

Yi Zhou1,2,3, Taojun Hu3,4,5, Yuji Sakamoto1

  • 1Division of Mathematics and Informatics, Graduate School of Human Development and Environment, Kobe University, Kobe, Japan.

Statistics in Medicine
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

Publication bias (PB) in rare-event meta-analysis is a challenge. This study introduces a new sensitivity analysis framework for generalized linear mixed models (GLMMs) to evaluate PB

Keywords:
binomial distributioncontinuity correctionhypergeometric distributionrandom‐effects meta‐analysisrare eventsselection function

Related Experiment Videos

Last Updated: May 13, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Publication bias (PB) is a significant concern in systematic reviews and meta-analyses, often stemming from selective academic literature publication.
  • Existing methods for addressing PB primarily rely on the normal-normal (NN) random-effects model, which assumes normal distribution of data and may be inadequate for rare-event meta-analyses.
  • Generalized linear mixed models (GLMMs) offer improved accuracy for rare-event data by using exact event distributions, but methods to address PB within GLMMs are limited.

Purpose of the Study:

  • To propose a novel framework for sensitivity analysis to evaluate the impact of publication bias (PB) in contrast-based generalized linear mixed models (GLMMs).
  • To extend existing Copas-Heckman-type sensitivity analysis methods to the GLMM context for rare-event meta-analysis.
  • To provide a practical and computationally efficient approach for assessing PB in meta-analyses involving rare events.

Main Methods:

  • Developed a sensitivity analysis framework based on Copas-Heckman-type methods, linking study-specific true effect sizes to a latent Gaussian variable on study sample sizes.
  • The proposed methods avoid the need for continuity corrections, enhancing applicability.
  • Methods are designed for easy implementation using standard statistical software with low computational demands.

Main Results:

  • Simulation studies demonstrated the proposed methods' effectiveness in adjusting for publication bias (PB).
  • Performance was compared favorably against related existing methods.
  • Real-world examples illustrated the framework's broad applicability in meta-analyses of odds ratios and proportions with rare-event outcomes.

Conclusions:

  • The proposed sensitivity analysis framework effectively addresses publication bias (PB) in generalized linear mixed models (GLMMs) for rare-event meta-analysis.
  • The methods offer a practical, computationally efficient, and broadly applicable solution for evaluating PB.
  • This work advances the methodology for robust meta-analysis in the presence of rare events and potential publication bias.