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Related Concept Videos

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Confounding in Epidemiological Studies01:27

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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...
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Bias in Epidemiological Studies01:29

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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:  
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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GRADE guidelines 33: Addressing imprecision in a network meta-analysis.

Romina Brignardello-Petersen1, Gordon H Guyatt1, Reem A Mustafa2

  • 1Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, L8S 4L8, Canada.

Journal of Clinical Epidemiology
|July 22, 2021
PubMed
Summary
This summary is machine-generated.

This guidance helps assess imprecision in network meta-analysis evidence certainty. It uses confidence intervals and optimal information size to ensure reliable results.

Keywords:
Certainty of evidenceGRADE guidanceImprecisionNetwork meta-analysisOptimal information size

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Area of Science:

  • Evidence-based medicine
  • Meta-analysis methodology

Background:

  • Network meta-analysis (NMA) synthesizes evidence from multiple treatment comparisons.
  • Assessing the certainty of evidence in NMA is crucial for clinical decision-making.
  • Imprecision is a key factor influencing the certainty of NMA estimates.

Purpose of the Study:

  • To provide GRADE (Grading of Recommendations Assessment, Development and Evaluation) guidance for assessing imprecision in NMA.
  • To standardize the evaluation of uncertainty in NMA results.

Main Methods:

  • Development of guidance through iterative discussions and computer simulations by a GRADE working group project team.
  • Presentations and approval at GRADE working group meetings.

Main Results:

  • Guidance focuses on using 95% confidence or credible intervals and optimal information size (OIS).
  • Rating down certainty is recommended if intervals cross a pre-specified threshold.
  • If intervals do not cross thresholds, OIS and effect size guide the decision; modest effects or met OIS do not warrant rating down for imprecision.

Conclusions:

  • This guidance facilitates appropriate assessment of imprecision in NMA.
  • Consistent application of this guidance will improve the reliability of certainty of evidence ratings in NMA.