Let's start at the beginning. Why do we do research and write papers? No, not just to get famous, tenured or funded. The fundamental task of science is to answer questions. The big questions of all time get broken down into infinitesimally small chunks that can be answered with experimental or observational scientific methods. These answers integrated together provide the model for life as we understand it.
Clearly, the question is the most important part of the equation, and this is why in my semester-long graduate epidemiology course on the evaluative sciences we spend fully the first four to five weeks talking about how to develop a valid and answerable question. The cornerstone of this validity is its importance. Hence, the first question that we pose is: Is the study question important?
This is a bit of a loaded question, though. Important to whom? How is "important" defined? This is somewhat subjective, yet needs to be scrutinized nevertheless. In the context of an individual patient, the question may become: Is the study question important to me? So, importance is dependent on perspective. Nevertheless, there are questions upon whose importance we can all agree. For example, the importance of the question of whether our current fast-food life style promotes obesity and diabetes is hard to dispute.
Regardless of how we feel about the importance of the question, we must first identify the said research question. At least some of the time you will be able to find it in the primary paper, buried in the last paragraph of the Introduction section. Most of the questions we ask relate to etiologic relationships ("etiology" is medicalese for "causation"). Now, you have heard many times that an observational study cannot answer a causal question. Yet, why do we bother with the time, energy and money needed to run observational studies? Without getting too much into the weeds, philosophers of science tell us that no single study design can give us unequivocal evidence of causality. We can merely come close to it. What does this mean in practical terms? It means that, although most observational studies are still interested in causality rather than a mere association, we have to be more circumspect in how we interpret the results from such studies than from interventional ones. But I am jumping ahead.
Once we have identified and established the importance of the question, we need to evaluate its quality. A question of high quality is 1). clear, 2). specific, and 3). answerable. The question that I posed above regarding fast food and obesity possesses none of these characteristics. It is too broad and open to interpretation. If I were really posing a question in this vein, I would choose a single well defined exposure (consuming 3 cans of soda per day) influencing a single outcome (10% body weight gain) over a specific period of time (over 30 weeks). While this is a much narrower question that the one I proposed above, it is only by answering bundles of such narrow questions and putting the information together that we can arrive at the big picture.
A general principle that I like to teach to my student is the PICO or PECOT model (I did not come up with it, but am its avid user). In PICO, P=population, I=intervention or exposure, C=comparator, and O=outcome. The PECOT model is an adaptation of the PICO for observations over time, resulting in P=population, E=exposure, C=comparator, O=outcome, T=time. These models can help not only pose the question, but to unravel the often mysterious and far from transparent intent of the investigators.
Once you have identified the question and dealt with its importance, you are ready to move on to the next step: evaluating the study design as it relates to the question at hand. We will discuss this in the next post.