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The architecture of clinical research

From the “Biostatistics and Epidemiology Lecture Series, Part 1”

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I am flattered to present the inaugural talk in the biostatistics and clinical research design series on the architecture of clinical research. This content is based on the teachings of my mentor, Dr. Alvan Feinstein, who together with Dr. David Sackett, is credited with pioneering clinical epidemiology. Dr. Feinstein was a Sterling Professor at the Yale School of Medicine. His main opus of work is a book called, Clinical Epidemiology: The Architecture of Clinical Research.1 This paper is named in credit to Dr. Feinstein’s enormous contribution. I will review some important terms defined by Dr. Feinstein to provide the background necessary for the remainder of the talks in this series.

To start, I will frame this topic by asking the following question: Why do we do research? I’ll talk about the basic structure of research studies and provide a taxonomy, as Dr. Feinstein would say, a nomenclature with which to understand trial design and the sources of bias in those trials. Then, I will discuss these sources of bias in detail using the taxonomy that Dr. Feinstein described in his aforementioned book. Finally, I will share with you some examples of bias in clinical trials to help you better understand these concepts.

Now, the answer to the basic question posed above is: basically, we do cause-and-effect research to establish the causality of a risk factor or the efficacy of a therapy. Does cigarette smoking cause lung cancer? Does taking hydrochlorothiazide help systemic hypertension? Does air pollution worsen asthma? Does supplemental oxygen help patients with chronic obstructive pulmonary disease (COPD)?

Cause-and-effect research can be subsumed under 2 broad issues: causal risk factors and therapeutic efficacy. In his review of early false understandings in medicine that were based on anecdotal observation alone, Thomas cites many examples—“the undue longevity of useless and even harmful drugs can be laid at the door of authority,” ie, empiricism, lack of rigorous research.2 The field is full of these: yellow fever causality, the value of cupping, and even intermittent mandatory ventilation when it was described by John Downs in 1973 and touted as a superior mode for weaning patients from mechanical ventilation.3 Twenty-five years later, randomized controlled trials by Brochard et al4 indicated not only that intermittent mandatory ventilation was not the best mode to wean but was, in fact, the worst mode for weaning patients from mechanical ventilation compared with either pressure support or spontaneous breathing trials. Many more examples exist to demonstrate the false understandings that can be ascribed to lack of rigorous study or evidence in medicine.

Design of a controlled trial according to Feinstein.
Figure 1. Design of a controlled trial according to Feinstein.1

Before systematically exploring the sources of bias in Feinstein’s construct, let us define some very basic terms from his book. Dr. Feinstein talks about the baseline state, which refers to the group of patients under study who are culled from a larger population to whom the results are intended to be applied (Figure 1).1 This baseline group is hopefully representative of this larger target population. As a nod to the later discussion, Dr. Feinstein would call bias introduced by unusual assembly of the study population from the larger intended population as “assembly bias.” So, if the group under study is not representative of either the patients you see or the world of patients with this condition or if there is something special or distinctively nonrepresentative about the study population, then the results may be subject to “assembly bias.” Assembly bias can compromise the so-called “external” validity of the study—its ability to be applied to populations beyond the study group.

Having assembled a baseline group for study, that group is classically allocated to 1 of 2 (or sometimes more than 2) compared therapies. In a controlled trial, patients can be allocated using a variety of strategies, including randomization. Using the paradigm diagram (Figure 1, which considers a 2-arm trial), patients are allocated to 1 of 2 compared groups—group A and group B. Then, in a treatment trial, 1 group receives the principal maneuver, which is the drug or intervention under study—for example, supplemental oxygen for patients with COPD. The comparative maneuver is allocated to group B, which also receives all the other treatments (called “co-maneuvers”) that are used to treat the condition under study. In a trial of supplemental oxygen for COPD evaluating lung function and exacerbation frequency as outcome measures, such co-maneuvers might include inhaled bronchodilators, inhaled corticosteroids, pulmonary rehabilitation, and Pneumovax vaccine. Ideally, these co-maneuvers are equally distributed between the compared groups (A and B).

So, in summary, we have a comparative maneuver, which is the nonadministration of supplemental oxygen in this proposed trial of supplemental oxygen in COPD, the principal maneuver—administration of oxygen—and all the co-maneuvers that are ideally equally distributed between both groups. This balanced distribution of co-maneuvers between the compared groups helps to ensure that any differences in the study outcome measures (ie, what is counted as the main impact of the intervention under study) can be solely attributed to the principal maneuver. When this condition—that the difference in outcomes can be reliably ascribed to the study intervention—is satisfied, the study is felt to be “internally” valid. As we will see, ensuring internal validity requires freedom from the many sources of what Dr. Feinstein calls “internal bias.”

Back to basic terms: “cohort” in Dr. Feinstein’s language is a group that shares common traits and is followed forward in a longitudinal study. The “outcome measure” is self-evident—it is what is being measured, with the “primary outcome” being the pre-defined measure that is considered the most important (and ideally most clinically relevant) impact of the study intervention. Later in this series of lectures, there will be discussions of power calculations and the so-called “effect size”—the magnitude of effect that the intervention is expected to produce and that is ideally deemed clinically important.

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