The very first sentence of the study paper reflects a common, and rather muddled, belief about placebo-controlled trials:
Placebo groups are used in trials to control for placebo effects, i.e. those changes in a person's health status that result from the meaning and hope the person attributes to a procedure or event in a health care setting.The best I can say about the above sentence is that in some (not all) trials, this accounts for some (not all) of the rationale for including a placebo group in the study design.
There is no evidence that “meaning and hope” have any impact on HbA1C levels in patients with diabetes. The placebo effect only goes so far, and certainly doesn’t have much sway over most lab tests. And yet we still conduct placebo-controlled trials in diabetes, and rightly so.
To clarify, it may be helpful to break this into two parts:
- Most trials need a “No Treatment” arm.
- Most “No Treatment” arms should be double-blind, which requires use of a placebo.
We need a “No Treatment” arm:
- Where the natural progression of the disease is variable (e.g., many psychological disorders, such as depression, have ups and downs that are unrelated to treatment). This is important if we want to measure the proportion of responders – for example, what percentage of diabetes patients got their HbA1C levels below 6.5% on a particular regimen. We know that some patients will hit that target even without additional intervention, but we won’t know how many unless we include a control group.
- Where the disease is self-limiting. Given time, many conditions – the flu, allergies, etc. – tend to go away on their own. Therefore, even an ineffective medication will look like it’s doing something if we simply test it on its own. We need a control group to measure whether the investigational medication is actually speeding up the time to cure.
- When we are testing the combination of an investigational medication with one or more existing therapies. We have a general sense of how well metformin will work in T2D patients, but the effect will vary from trial to trial. So if I want to see how well my experimental therapy works when added to metformin, I’ll need a metformin-plus-placebo control arm to be able to measure the additional benefit, if any.
All of the above are especially important when the trial is selecting a group of patients with greater disease severity than average. The process of “enriching” a trial by excluding patients with mild disease has the benefit of requiring many fewer enrolled patients to demonstrate a clinical effect. However, it also will have a stronger tendency to exhibit “regression to the mean” for a number of patients, who will exhibit a greater than average improvement during the course of the trial. A control group accurately measures this regression and helps us measure the true effect size.
So, why include a placebo? Why not just have a control group of patients receiving no additional treatment? There are compelling reasons:
- To minimize bias in investigator assessments. We most often think about placebo arms in relation to patient expectations, but often they are even more valuable in improving the accuracy of physician assessments. Like all humans, physician investigators interpret evidence in light of their beliefs, and there is substantial evidence that unblinded assessments exaggerate treatment effects – we need the placebo to help maintain investigator blinding.
- To improve patient compliance in the control arm. If a patient is clearly not receiving an active treatment, it is often very difficult to keep him or her interested and engaged with the trial, especially if the trial requires frequent clinic visits and non-standard procedures (such as blood draws). Retention in no-treatment trials can be much lower than in placebo-controlled trials, and if it drops low enough, the validity of any results can be thrown into question.
- To accurately gauge adverse events. Any problem(s) encountered are much more likely to be taken seriously – by both the patient and the investigator – if there is genuine uncertainty about whether the patient is on active treatment. This leads to much more accurate and reliable reporting of adverse events.