Some Thoughts About Clinical Research

Clinical research requires a wide range of skills. These skill include the ability to work with a wide range of people, to lead teams with people from wide and vastly different backgrounds, to design appropriate studies, to ask right questions, to understand research methods specific to the study question, to develop in-depth content expertise in the area of research focus, to get funding for research projects, to present study results at national meetings, to write manuscripts for publication in peer-review journals, and so on and so forth.

A fundamental skill for a researcher is the ability to knit together the conceptual framework for a study (theory) with appropriate measurement, with the result either supporting or opposing the conceptual framework. The theory should be based on the most current state of knowledge, the data collected should have the ability to test the theory, the statistical models should reflect both the conceptual structure hypothesized to have given rise to the data and the nature of collected data, and the inferences should be based on the data and the tested statistical models. This process is not linear, rather it is a loop in which theoretical aspects inform the collection of data and results of the data analyses help in refining the theory, which generates more testable hypothesis, additional data collection, and so on.

Most research is probabilistic, as opposed to deterministic. In other words, the results we obtain are not always certain; we have to include uncertainty in our analyses and expect some uncertainty in our results and inferences. Thus, we have to accept that our results are unlikely to be laws governing the system we plan to study and more likely to be an approximation of what we expect to find in the real world, with some uncertainty. There are many reasons and sources of this uncertainty some of which can be addressed while others may still be there despite our best attempts.

A researcher should determine whether the interest of research is to build inferences at population level or at the level of individuals unit (often a patient in clinical research). The study design, data collection and analysis, and the inferences may be quite different depending on what is the object of our interest. While we study individuals, our results usually address inferences at population level. In general, it is much easier to predict about the response at a population level, that is on an average, individuals with higher body mass index (say >30) will have higher blood glucose than (say) 126mg/dL. However, it is much difficult to predict with certainty how likely a particular individual with a BMI>30 is to have higher blood glucose level than 126 mg/dL. For a predictor to work well at an individual level, among other things effect size needs to be quite large.

Another important concept is that of causality. While we often have a conceptual model in our mind that A is caused by B, it may be quite difficult to prove except perhaps in a clinical trial setting. There are several factors that can increase the likelihood that the direction of cause and effect in our conceptual model is correct, such as temporality and biological plausibility.  However, often there remains a possibility that B is in fact caused by A or that some other unknown (or unmeasured) factor, C, may be responsible for both A and B. Hence, we often claim an association or correlation between A and B and not causality.