Part 2 book “Veterinary epidemiology” has contents: Demonstrating association, observational studies, design considerations for observational studies, clinical trials, validity in epidemiological studies, systematic reviews, diagnostic testing, surveillance, statistical modeling, mathematical modeling, and other contents. | 383 18 Validity in epidemiological studies The goal of epidemiology is to generate and interpret information about disease and health in populations in order to aid decision making. In an ideal world each research question would be addressed by a particular study, the study would provide an exact representation of the relevant domain, and the study results would provide the information needed to truthfully answer the question. Unfortunately, this is never the case. In truth, all studies provide flawed depictions of ‘reality’. Maclure and Schneeweiss (2001) imagine epidemiological studies of causation to be like a telescope used to observe populations – they call this the Episcope. The Episcope is made up of a number of filters and lenses, each of which is imperfect and therefore distorts the image to a greater or lesser extent. A simplified version of Maclure and Schneeweiss’ Episcope is shown in Figure . This has eight lenses representing the key issues affecting the validity of epidemiological studies (although these could be thought of as compound lenses, each containing an array of imperfect lenses): 1. 2. 3. 4. 5. 6. 7. 8. background factors; interpretation biases; selection biases; statistical interaction and effect-measure modification; information biases; confounding; errors in analysis; communication biases. Each of these lenses will induce an amount of error and the resulting image of reality will be imperfect. An unavoidable problem is that we can only compare this image with other imperfect images, and hence we cannot say exactly where, how and to what extent the image differs from reality. The role of the epidemiologist is to minimize the error associated with each of these lenses as best as possible and then to understand where residual errors remain and the potential impact of these on the results. By doing this we can get closer to knowing the true situation. Although it is often useful to think about these main sources of error in .
