Abstract
Awareness of potential biases is important for both researchers and policy-makers in public health: for researchers when designing and conducting studies, and for policymakers when reading study reports and making decisions. This paper explains the meaning and importance of epidemiologic bias in public health and discusses how it arises and what can be done to minimize it. Examples of counting participants in a meeting, to which many policy-makers can relate, are used throughout the paper to illustrate bias in general, random error and systematic error, the effect of sample size, the three main categories of bias (selection, information and confounding), stratification and mathematical modeling.
MeSH terms: Bias (epidemiology), epidemiology, selection bias, observer variation; confounding factors (epidemiology)
Résumé
Il importe que les chercheurs et les décideurs en santé publique soient conscients des risques de biais. Les chercheurs doivent en tenir compte lorsqu’ils conçoivent et effectuent des études, tout comme les décideurs lorsqu’ils lisent des rapports d’étude et prennent des décisions. Le présent article explique la signification et l’importance du biais épidémiologique en santé publique et montre comment il survient et ce qu’on peut faire pour l’atténuer. Des exemples de comptabilisation des participants à une réunion, exemples proches de la réalité de nombreux décideurs, sont utilisés tout au long de l’article pour illustrer le biais en général, l’erreur aléatoire et l’erreur systématique, l’effet de la taille de l’échantillon, les trois principales catégories de biais (sélection, information et facteurs de confusion), la stratification et la modélisation mathématique.
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