Origins and Fundamentals of MBE – Part 2: Understanding the real importance of learning to critically analyze the validity of scientific evidence

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As we saw previously , the evidence-based medicine (EMB) movement was born as an educational initiative at McMaster, where young medical residents were taught to critically analyze a study. scientific. Thus, it would be possible to be more able to make scientifically informed decisions and not only based on individual opinion. Thus, the second fundamental principle of the MBE states that it provides guidance for deciding whether the evidence is more or less reliable. When referring to the confidence that one can have in relation to evidence, this principle emphasizes the existence of explicit and objective criteria that assess the methodological validity of products arising from clinical research, notably individual clinical studies, systematic reviews and guidelines.

Although there are currently several models of clinical studies, in most cases we come across the following: randomized studies, sectional studies ( or cross-sectional), case-control studies and observational cohort studies. As for the critical analysis of the methodological validity, we should check at beginning, at the middle and in The end of each study.

Read too: Evidence-based medicine: what are the practical barriers and how to apply it?

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Critical analysis of the methodological validity of randomized studies

At the beginning of the study we must first assess whether the randomization was actually performed and if so, check if there was masking or concealment. In practice, randomization involves the generation of a sequence by which study participants are randomly allocated to study groups, ensuring the unpredictability of this allocation sequence. The second masking process, which is nothing more than allocation concealment, prevents those involved in conducting a study from being able to influence (even if hypothetically) in determining which group the participants will be referred to. Randomization aims to balance known and unknown prognostic factors, making the groups comparable at the beginning of the study. In general, automated response systems are used for this purpose, most commonly a system telephone (IVRS) or digital ( IWRS).

In general, randomized studies provide a table with the description of demographic data in the different study groups. This table allows us to assess whether the prognostic factors, for example associated diseases, are balanced in the groups, that is, whether randomization has fulfilled its role.

In the middle of the study we have to see if there was blinding, which consists of the impossibility of knowing which group the participant was allocated to. In general the term double blinded refers to the blinding of research participants and professionals who monitor them directly during the study. However, it is also important that all those who analyze the study data and those who judge and audit the veracity of the clinical outcomes that occur throughout the study are “blind” to prevent their judgment from being influenced by the science of allocation of the participant who suffered the outcome in question. Also, in the middle of the study, it is important to check whether the co-interventions were balanced in the groups. This means that all treatments, known to be recommended for a condition in question, are being carried out in a proportionally equal manner in the studied groups. If a group receives in greater intensity, for example, a certain co-intervention that is able to reduce the rate of events considered study outcomes, the study result can be determined by this and not by the intervention being studied. Also in the middle of the study, the level of follow-up (including the assistance routines for visits and the care infrastructure) must be the same for all groups. Thus, avoiding that a group is privileged with better care that could imply a reduction in its rate of events or outcomes.

At the end of the study, we have to check if there was a analysis by intention to treat ( intention to treat), that is, whether the observed outcomes were assigned to the group to which the research participant who suffered the outcome was allocated. This does not depend on whether the participant actually received the intervention related to the group he is in or not. Any event that occurs from the moment the allocation took place is considered to be within the respective group. Another point to be seen at the end of the study is the loss to follow-up (lost of follow up), that is, the percentage of participants that in each group did not complete the study. There is no “magic number” as even a 2% loss to follow-up (1% in each group) can be considered relevant. Especially if the absolute difference in event rate between groups is small, type less than 1%. For this check, a sensitivity analysis must be performed, in which the difference in the rate of events between the groups is recalculated, in simulated scenarios.

In one scenario, it is considered that, within one of the groups, all those without follow-up suffered the outcome, whereas in the other group, it is considered that none of those without follow-up had any outcome and then the results are recalculated. In another scenario, this attribution of outcomes is inverted and the results are recalculated again. Finally, at the end of the study we should see if there was early interruption of the study for benefit, that is, before the deadline initially stipulated in the study protocol. In clinical studies, it is important to perform interim analyzes at predetermined intervals throughout the study. Such analyzes consist of checking the rate of events/outcomes in each study group during the study. The goal is to see if the tested intervention, which is supposed to have a beneficial effect, is not causing harm.

If the rate of undesirable events is very high in the group submitted to the tested intervention, above what is considered acceptable, it should be for ethical reasons to discontinue the study, assuming that the intervention does more harm than good. Now, if in an interim analysis it is observed that in the intervention group there is a much lower rate of outcomes than that observed in the control group, one should not assume that the intervention is beneficial and stop the study too soon, as this difference can be the result of chance, simply because of the small number of events. Studies terminated early for benefit tend to magnify and overestimate the real effect of the tested interventions, which may not even exist, when compared to studies that are completed according to their initial planning.

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