TY - CHAP M1 - Book, Section TI - Chapter 15. Analytical Methods of Addressing Confounding A1 - Reyes, Eric M. A1 - Thomas, Laine E. A2 - Lopes, Renato D. A2 - Harrington, Robert A. PY - 2013 T2 - Understanding Clinical Research AB - Chapter 13 discussed many of the obstacles inherent in the analysis of data from observational studies, with bias due to confounding arguably the largest of these concerns. Confounding can be seen as an issue of “mistaken identity,” in which the cause of an observed effect is attributed to the wrong party. As an example, consider a cohort study undertaken to assess the efficacy of a treatment. In this cohort, younger people are more likely to receive the treatment and are less likely to experience the outcome of interest (Figure 15–1). If a treatment effect is observed, it is unclear whether the observed effect is due to the treatment or to the younger age of the patients receiving the treatment. That is, age and treatment are said to be confounded; equivalently, age is said to be a confounder. Formally, a confounder is any variable related to both the outcome of interest and the treatment under study. In our example, age affects both the event rate and which treatment the person receives. SN - PB - The McGraw-Hill Companies CY - New York, NY Y2 - 2024/03/29 UR - accessbiomedicalscience.mhmedical.com/content.aspx?aid=57836740 ER -