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Related Concept Videos

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:
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...
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:
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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...
Halo Effect01:27

Halo Effect

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...
Surveys02:16

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

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Related Experiment Video

Updated: May 8, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

Do simple screening statistical tools help to detect reporting bias?

Romain Pirracchio1, Matthieu Resche-Rigon, Sylvie Chevret

  • 1Department of Anesthesiology and Critical Care Medicine, Hôpital Européen Georges Pompidou, Université Paris-Descartes Sorbonne Paris Cité, Paris, France. romainpirracchio@yahoo.fr.

Annals of Intensive Care
|September 6, 2013
PubMed
Summary
This summary is machine-generated.

Simple methods can help appraise randomized controlled trial (RCT) reporting quality. Analysis revealed inconsistencies in reported p values and variable distributions, suggesting potential issues in published research claims.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 8, 2026

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Research Integrity

Background:

  • Reporting bias and potential fraud can lead to a majority of published research claims being false or misunderstood.
  • Appraising the quality of reporting in randomized controlled trials (RCTs) is crucial for reliable scientific evidence.
  • Simple methods are proposed to aid in the critical evaluation of RCT reporting.

Purpose of the Study:

  • To introduce and illustrate simple methods for evaluating the reporting quality of randomized controlled trials (RCTs).
  • To identify potential discrepancies and warning signals in published RCT data.
  • To provide a framework for justifying requests for raw data access when reporting quality is questionable.

Main Methods:

  • The evaluation roadmap involves four steps: assessing variable distributions, evaluating p-value distributions, and performing data simulation using parametric bootstrap with explicit p-value computation.
  • The proposed methods were applied to published data from a retracted RCT comparing hydroxyethyl starch and albumin-based priming for cardiopulmonary bypass.
  • Key analyses included checking for normality assumptions, testing p-value uniformity, and comparing calculated vs. reported p-values.

Main Results:

  • Several variables were presented as normally distributed despite clear non-normal distributions.
  • The distribution of 16 p-values for baseline characteristics deviated from uniformity (p = 0.045).
  • Explicit computations revealed discrepancies in reported p-values for urine output and packed red blood cells (PRBC) transfused during surgery, with calculated p-values differing significantly from reported ones.

Conclusions:

  • The proposed simple evaluation methods can serve as valuable warning signals regarding RCT reporting quality.
  • These methods do not definitively prove error or fraud but are essential for justifying requests for raw data access.
  • Highlighting reporting inconsistencies encourages greater transparency and rigor in scientific publications.