Received Jun 3; Accepted Dec The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Introduction There is a continuous need to identify safe, effective treatments and vaccines which will have a significant impact.
Association and Causation Determining if there is an association between an exposure and an outcome is one of the fundamental goals in all biomedical research. Signals and Evidence Signals can arise when examining events that occur after taking drugs from an observed association.
Conclusion Despite the need to identify effective and safe treatments as rapidly as possible in the current crisis, it is important to make clear distinctions between observed associations and causal associations.
Author Contributions All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Burger L. Human trials of British coronavirus vaccine to reach 10, London, United Kingdom: Reuters.
Convalescent plasma or hyperimmune immunoglobulin for people with COVID a living systematic review. Cochrane Database Syst. Lancet Infect. Science Safety signal. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Themes Epidemiol. Hydroxychloroquine and azithromycin as a treatment of COVID results of an open-label non-randomized clinical trial. Agents 56 , Heart Assoc. The environment and disease: association or causation?
First case of novel coronavirus in the United States. Oxford, United Kindom: Oxford. Tocilizumab, an anti-IL6 receptor antibody, to treat Covidrelated respiratory failure: a case report. Lancet Digital Health 2 , e—e Association: Is a specified health outcome more likely in people with a particular "exposure"? Is there a link? Association is a statistical relationship between two variables. Two variables may be associated without a causal relationship. For example, there is a statistical association between the number of people who drowned by falling into a pool and the number of films Nicolas Cage appeared in in a given year.
However, there is obviously no causal relationship. Jewish women have a higher risk of breast cancer, while Mormons have a lower risk. However, one's religion is not a cause of breast cancer.
There are other explanations. Causal diagram illustrating the structure of collider bias. It is plausible that both joint trauma and knee osteoarthritis lead to surgical intervention, such as knee arthroscopy the collider. That is, individuals who suffer a traumatic joint injury or those with a diagnosis of knee osteoarthritis are likely to undergo knee arthroscopy. In this case, if the collider knee arthroscopic surgery is controlled for by study design or analysis , we will observe a distorted association between joint trauma and knee osteoarthritis Figure 4.
Figure 4. Causal diagram illustrating a distorted association between joint trauma and osteoarthritis by controlling for the collider, exposure to arthroscopic surgery. Collider bias could be induced if, for instance, researchers only gain access to data from those who have undergone surgical intervention which would induce selection bias — a form of collider bias.
Or if researchers have access to the entire dataset, but mistakenly decide to statistically control for surgical intervention during analysis. In effect, both mistakes will induce a biased association between joint trauma and knee osteoarthritis. When we study a group of individuals who received surgery only as a result of joint trauma or knee osteoarthritis , knowing that a patient underwent surgery because of joint trauma will tell us that the patient is less likely to have knee osteoarthritis and vice versa.
In other words, knee osteoarthritis becomes dependent on joint trauma within a sample of patients who undergo surgery even though they are independent in the wider population. So, colliders induce bias when they are controlled for, whereas confounders induce bias when they are left uncontrolled. To eliminate both of these biases, we need to identify as many confounders as we can and control for them during the analysis, while at the same time identifying all colliders and leaving them uncontrolled.
Associations can represent causal effects, but only when we adequately control for all confounders, do not control for any colliders, and establish temporal precedence of the exposure and outcome. Even then, unknown confounders and colliders and other biases may vitiate our conclusion. Ruling out confounders and colliders is a key step in establishing causation but is insufficient alone.
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