Bias in Clinical Studies of Genetic Diseases
Susan Stuckless, Patrick S. Parfrey
Abstract
Susan Stuckless, Patrick S. Parfrey
Abstract
Clinical epidemiological research in genetic diseases entails the assessment of phenotypes, the burden and etiology of disease, and the efficacy of preventive measures or treatments in populations. In all areas, the main focus is to describe the relationship between exposure and outcome and determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and be able to assess their impact on the magnitude and direction of the observed effect. This chapter reviews the epidemiological concepts of selection bias, information bias, and confounding and discusses ways in which these sources of bias can be minimized.
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Clinical epidemiological research in genetic diseases entails the assessment of phenotypes, the burden and etiology of disease, and the efficacy of preventive measures or treatments in populations. In all areas, the main focus is to describe the relationship between exposure and outcome and determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and be able to assess their impact on the magnitude and direction of the observed effect. This chapter reviews the epidemiological concepts of selection bias, information bias, and confounding and discusses ways in which these sources of bias can be minimized.
Key concepts: Confounding, Selection bias, Information bias, Epidemiology, Etiology, Disease, Medicine, Selection (genetic algorithm)