Related Experiment Video

Updated: Jan 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.7K

Frequent inappropriate use of unweighted summary statistics in systematic reviews of pathogen genotypes or genogroups

Linh Tran1, Mai Nhu Y2, Thai Le Ba Nghia2

  • 1Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam.

Journal of Clinical Epidemiology
|November 20, 2019
PubMed
Abstract

Insights

This study evaluated the quality of epidemiological systematic reviews (SRs) and meta-analyses (MAs) on pathogen genotypes. Meta-analysis showed higher quality than summary statistics, remaining the most suitable method for robust conclusions.

Area of Science:

  • Epidemiology
  • Microbiology
  • Biostatistics

Background:

  • Methodological quality of systematic reviews (SRs) and meta-analyses (MAs) in pathogen genotype studies is crucial for reliable conclusions.
  • Assessing the quality of different synthesis methods is essential for advancing epidemiological research.

Purpose of the Study:

  • To systematically assess and report the methodological quality of epidemiological SRs and MAs focusing on pathogen genotypes/genogroups.
  • To compare the quality of meta-analysis (MA), summary statistics, and SR-only approaches.

Main Methods:

  • A systematic search of nine electronic databases and reference lists identified 36 relevant studies.
  • Studies were categorized into MA (weighted pooling), summary statistics (unweighted analysis), and SR only (no pooling).
  • Methodological quality was assessed using AMSTAR, PRISMA, and ROBIS tools.

Main Results:

  • Meta-analysis (MA) studies demonstrated higher methodological quality compared to those using summary statistics, based on AMSTAR and PRISMA scores.
  • SR-only and summary statistics groups showed similar quality scores across AMSTAR, PRISMA, and ROBIS.
  • The overall methodological quality of epidemiological studies has improved between 1999 and 2017.

Conclusions:

  • Meta-analysis (MA) is the most suitable method for drawing rational conclusions in epidemiological studies of pathogen genotypes/genogroups, despite the common use of unweighted summary statistics.
  • Improving the quality of systematic reviews and meta-analyses is vital for public health.
Keywords:
GenogroupsGenotypesMeta-analysisMethodological qualitySummary statisticsSystematic review

More Related Videos

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.3K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K

Related Concept Videos

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
523
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
851
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
634
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
540