Pragmatic clinical trial.
We all know how difficult it is to make a new drug available to people who can benefit from it. From the moment a promising molecule is identified and somebody thinks that it may be useful, until it can be bought in a pharmacy, a long journey goes by that, at present, usually does not last less than 10 or 12 years.
During this long journey, the future drug, after its initial development phase, enters the thorny path of the preclinical phase, with studies in cell or animal models, followed by a gradual use in humans to verify its toxicity, its right dose and its effectiveness.
The phases of the development of a new drug
Thus, future drugs go through a series of phases. First contact with humans takes place in phase I trials, in which a small number of people, usually volunteers, act as lab rats to study the pharmacokinetics and pharmacodynamics of the new drug, along with the safety of the doses administered.
It is in phase II when the drug is usually administered for the first time to patients who could be candidates for treatment. These studies try to study the benefits and the optimal dose in comparisson with a control group.
Finally, before the drug is launched and commercialized, phase III trials are conducted. In these trials, the drug is tested in a controlled way against a placebo or the usual treatment in a large number of patients. Its objective is to determine the efficacy, toxicity and risk-benefit of the intervention with the goal of obtaining its authorization for the studied indication.
An estimated 5,000-10,000 promising molecules are identified each year, of which only about 10 make it to human trials. And even so, when the drug ends up being marketed and used in a massive way, we can find surprises regarding its effectiveness, safety and difficulties of use.
The rigidity of randomized clinical trials
It is not uncommon for an adverse effect that was previously not seen in phase III trials during the time of drug development to be described when the drug is used massively.
We can put part of the blame that these things happen in our inseparable companion: random. Imagine a drug that triggers one fildulastrosis for every 50,000 patients who receive it. If we do a phase III trial with 500 treated patients, the probability that we can detect at least one case of this complication is around 1%.
Logically, it will not be strange that we do not realize the problem until the drug is used massively. And some of you will think that the remedy is to increase the number of participants in phase III trials, but it is not that simple. There are more considerations to take into account.
Clinical trials are usually conducted in very restrictive situations. They are usually limited to a highly selected population, from which the most severe patients or those with a higher risk of complications are usually excluded, who are often the ones that interest us clinicians the most. Furthermore, the entire design is optimized to study the isolated effect of the intervention under study. This is fine to avoid bias and to ensure that the methodological quality of the trial is adequate, but it can greatly limit the applicability of the trial’s results to patients in our usual clinical setting.
So, after phase III and commercialization, we need to be able to evaluate the new drug in a situation more similar to our day-to-day practice with our usual patients. A situation that is like life itself.
Observational studies are of no use for this task
It could be thought that a well-designed observational study would allow exploring the effectiveness of the drug in day-to-day clinical practice, without altering the patient’s usual life. Their results would be easier to generalize to more diverse populations. However, we already know that observational studies are subject to biases that can compromise the validity of their results.
When we compare the results of the two arms of an observational study, the difference detected may be due not only to the effect of the intervention or exposure under study, but also to a multitude of other factors that we call confounding factors.
If we know what these confounders are, we can adjust them during the study design or during the analysis phase. The problem is that there can always be factors that we are unaware of and we could end up attributing to our intervention an effect that may be caused or influenced by an unknown confounding variable.
Herein lies the great merit of randomization, which tends to homogeneously distribute the confounding factors between the two groups, both known and unknown. Thus, the difference that we observe at the end of the follow-up will be due to the only thing that is different between the two groups: the presence or absence of intervention.
We therefore see that we cannot do without clinical trials, although we can modify their design so that they are not so strict.
The pragmatic clinical trial: like life itself
The randomized clinical trial is the gold standard for epidemiological designs. As we have already said, it is usually carried out on a sample of participants with strict criteria, which are randomly divided into two groups, intervention and control, to see the differences between the two at the end of the study, which will be attributable to the intervention.
But we must bear in mind that, in addition to the pharmacological effect of the intervention, it can have effects on the behavior of participants and researchers that, in turn, can influence the way in which the study data are collected and the conclusions reached. To try to minimize these effects, which are often called extraneous effects, clinical trials resort to blinding.
This type of approach is called explanatory, and it is common in phase III trials and many of the post-marketing trials. They are very robust from a methodological point of view and focus especially on the “isolated” effect of the drug. Its problem, we have already said, its lower external validity or applicability to the normal situation of daily life.
As an alternative to the explanatory clinical trial, the pragmatic clinical trial arises, which does not focus so much on the isolated effect of the drug, but tries to also take into account the external effects that we have mentioned in order to obtain a broader estimate of efficacy that better reflects use in the real world.
Explanatory clinical trial vs pragmatic clinical trial
Now that we know the two approaches, the explanatory and the pragmatic ones, let’s see how these two types of clinical trials differ. Let us say before beginning that the two approaches are not exclusive, but rather constitute the two ends of a continuum in which the designers of the study will be able to position themselves according to their interests.
The three aspects that will define a trial its more explanatory or pragmatic soul will be the definition of the treatment or intervention, the assessment of the results, and the selection of study participants.
Treatment definition
The external effects that are not a direct consequence of the intervention can be homogenized in the two groups in the explanatory approach or to be included as a global effect of the intervention in the pragmatic approach.
Let’s imagine we try a new drug. In real life, the patient takes other treatments, has different lifestyles, may have difficulty paying for the treatment, etc. All of these factors can be strictly controlled in an explanatory approach, thus focusing on the effect of the drug.
On the contrary, the pragmatic approach allows the situation to be the real one of the patient, thereby assessing the overall effect of the intervention and everything that surrounds it. In this context, we could dispense with blinding, with which we would add the change in attitudes that can occur in the doctor or in the patient derived from using the drug. Although in one trial this implies an increased risk of information biases, it is the real situation that we would face in our practice.
Assessment of the study results
This is related to the choice of the primary outcome variable for the trial.
The explanatory approach will choose a variable with greater significance from the pathophysiological point of view. This may be easier to interpret and more objective, in addition to providing information on the biological characteristics of the drug under study. However, we can choose a pragmatic approach and select an outcome variable that is more important to the daily patient’s life.
Selection of participants
If we want to maximize the probability of demonstrating the effect of the intervention, we will opt for an explanatory approach. We will select the participants with strict inclusion and exclusion criteria.
If, on the contrary, our goal is to obtain results that are easily generalizable to our patients, these criteria should be less strict so that the study participants are more like our patients. Thus, the study will be more useful for decision-making in our usual clinical practice.
In summary
To conclude, we can summarize the main objective behind a pragmatic approach when designing a clinical trial: to assess the global effect of a treatment strategy in the real world.
For this, it will be necessary to randomize participants who are similar to the target population likely to receive the intervention, establish a design similar to that of routine clinical practice, and choose an outcome measure that is useful in daily practice.
But let’s not get confused. The fact that a clinical trial has a pragmatic approach does not mean that it will be easier, faster or cheaper to carry out than an explanatory one. The complexity will depend on the type of disease and the stage of development and type of intervention that we want to study.
We’re leaving…
And with this we are going to end for today.
We have already said that pragmatic and explanatory are the two extremes of a continuum and that usually no trial can be strictly pigeonholed into either of the two extremes.
So much so, that there are ways to quantify all the aspects that we have developed in this post to give a more or less pragmatic approach to the study in its design phase, as is the case with the PRECIS-2 tool. But that is another story…