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Bias01:22

Bias

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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...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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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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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...
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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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Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use.

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Publication bias in meta-analyses can skew results. Classical funnel plot methods have limitations; selection models offer a more realistic approach to assessing bias, especially with heterogeneous effects.

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Publication bias is a critical concern in meta-analyses, potentially compromising findings.
  • Funnel plot methods (Egger's test, Trim-and-Fill) are widely used but have limitations, particularly with effect heterogeneity.
  • These classical methods often assume bias affects small studies but not large ones.

Purpose of the Study:

  • To highlight the limitations of classical funnel plot methods for publication bias assessment.
  • To introduce and advocate for the use of selection models in meta-analyses.
  • To demonstrate the advantages of selection models over traditional methods.

Main Methods:

  • Review of limitations of classical funnel plot methods.
  • Description and interpretation guidance for selection models.
  • Application of selection models to a published meta-analysis for comparative insights.

Main Results:

  • Classical funnel plot methods have significant limitations, especially with heterogeneous effects.
  • Selection models assume bias favors statistically significant results and handle heterogeneity effectively.
  • Selection models provided unique insights not obtainable through funnel plot methods in the example meta-analysis.

Conclusions:

  • Meta-analyses should incorporate selection models alongside or instead of classical funnel plot methods.
  • Selection models offer a more robust approach to assessing publication bias, accommodating effect heterogeneity.
  • Improved reporting practices for publication bias assessment are needed to include methods like selection models.