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Bias01:22

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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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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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The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
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Finding the power to reduce publication bias.

T D Stanley1, Hristos Doucouliagos2, John P A Ioannidis3

  • 1Julia Mobley Professor of Economics, Hendrix College, Conway, AR, 72032, U.S.A.

Statistics in Medicine
|January 28, 2017
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Summary

This study introduces a new method, the weighted average of adequately powered (WAAP) estimates, to reduce bias from selective reporting in meta-analyses. WAAP proves more effective than traditional random-effects (RE) estimators when studies are selectively reported.

Keywords:
meta-analysispublication biasrandom-effectsstatistical powerweighted least squares

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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Statistical Inference

Background:

  • Selective reporting bias is a significant issue in meta-analyses, potentially skewing results.
  • Conventional random-effects (RE) estimators may be susceptible to this bias.
  • Statistical power is often insufficient in published medical research, complicating meta-analytic approaches.

Purpose of the Study:

  • To investigate how focusing on statistical power can mitigate selective reporting bias in meta-analyses.
  • To introduce and evaluate the weighted average of adequately powered (WAAP) estimator as an alternative to the RE estimator.
  • To address meta-analyses with low statistical power.

Main Methods:

  • Introduction of the weighted average of adequately powered (WAAP) estimator.
  • Simulation studies comparing WAAP with the conventional random-effects (RE) estimator.
  • Exploration of an alternative unrestricted weighted least squares weighted average for low-power meta-analyses.

Main Results:

  • WAAP demonstrates smaller bias than RE when selective reporting is present, without compromising other statistical properties.
  • The performance difference between RE and WAAP is negligible in the absence of selective reporting.
  • Significant bias can persist in all weighted averages under severe selective reporting or high heterogeneity.
  • A majority of medical meta-analyses lack adequate statistical power (>80%).

Conclusions:

  • The WAAP estimator offers an improved approach to handling selective reporting bias in meta-analyses with sufficient statistical power.
  • For meta-analyses with low statistical power, documenting this limitation and employing an unrestricted weighted least squares approach is recommended.
  • Addressing statistical power is crucial for enhancing the reliability of meta-analytic findings in medical research.