Pre-legalization, Indigenous communities identified a lack of culturally-specific educational materials on the health effects of cannabis; these should be developed, and could be implemented as a harm reduction strategy . Increased treatment availability will also be important. Given the high prevalence of use among youth, this population may be at increased risk of developing cannabis use-related problems ; early-onset use, in addition to high frequency use and use of high-potency products, has been identified as a major risk factor for subsequent health harms . At present, evidence-based treatment options in Canada for cannabis use disorder are limited . Ensuring Indigenous Canadians’ access to treatment can begin with increased access and funding for culturally-specific mental health and addictions services, as a gap in these services has been identified by Indigenous communities . Our systematic review has several potential limitations. First, our review focused on the prevalence of cannabis use and its associated factors in Indigenous Canadians, but we restricted inclusion to publications that reported data on cannabis use prevalence. Therefore, our review may not represent the full scope of research on factors associated with cannabis use in this population. Second, publications focusing on groups other than on-reserve First Nations were limited in number and had small sample sizes, reducing the generalizability of our findings. Third, bias affecting external validity was common among our included publications; all but two had at least one item at high risk of bias in this section. To increase the generalizability of our findings, we focused our summary of prevalence results on publications with the largest sample sizes and the fewest items at high risk of bias. Fourth, the interpretability of our findings on associated factors is limited given the largely cross-sectional nature of the available data, which renders the directionality of associations unclear.Because of the massive scale of the Covid-19 pandemic, Covid treatment research is subject to intense politicization, frequent media scrutiny, and continued public interest.
As thoroughly described in a recent JAMA Viewpoint Article , public scrutiny into drug development research has the potential to introduce a new set of incentives into the research process, which can, in turn, disrupt science-based regulation and the delivery of evidence-based treatments. These dangers became abundantly apparent through the US experience with hydroxychloroquine. When influencers and politicians began to endorse hydroxychloroquine as a treatment for Covid based upon early observational and preclinical studies, many in the public, including patients, physicians, and policy-makers, were quick to embrace hydroxychloroquine as an effective treatment, even though observational and preclinical studies are incapable of causally proving a drug’s safety or efficacy. This unearned enthusiasm for hydroxychloroquine led to shortages for those who required the drug for approved indications and even cases of poisonings . Another observational study subsequently found a positive association between hydroxychloroquine use and mortality as well as other adverse events, which may have made recruitment for hydroxychloroquine randomized controlled trials more challenging . Concerningly, the cacophony of contradictory observational and preclinical evidence presented in the media led some members of the public to adopt a dogmatic attachment to the drug’s effectiveness or ineffectiveness in line with their political identity . Since hydroxychloroquine was first suggested as a possible Covid treatment, a large-scale RCT, similar to what would be required for FDA drug approval, along with five smaller RCTs have all failed to find that hydroxychloroquine is an effective treatment for Covid. The authors of the large-scale RCT stated on June 5th, “this result should change medical practice worldwide and demonstrates the importance of large, randomised trials to inform decisions about both the efficacy and the safety of treatments” . Despite this causal evidence, many in the public still believe that hydroxychloroquine is an effective treatment , detracting from other potentially effective preventive measures and treatments and fueling conspiratorial theories about pharmaceutical interventions overall .Owing to the political and social history of cannabis grow facility, the safety and efficacy of medical cannabis and cannabis derived products is a political, as well as scientific, discourse. Many patients, physicians, and policy-makers want cannabis to be a safe and effective medication and are willing to endorse cannabis’ safety and efficacy with little supporting evidence . Media outlets frequently and widely cover the results of cannabis research, and like with hydroxychloroquine, many in the public are primed to accept favorable findings, regardless of their methodologies, as truth.
Because observational and preclinical studies generally take less time and cost less money than large-scale RCTs, interested parties, particularly “Big Marijuana” companies, are able to sponsor dozens of non-causal studies and publicize their findings, providing more ammunition to their political allies . The dissonance between positive observational trial results and federal cannabis prohibition have caused many in the public to form their own conclusions about the underlying motives for cannabis policy . Some become distrustful of the actors and systems instituting prohibition, including policy-makers, pharmaceutical regulators, and the pharmaceutical industry. Because their situations are similar, medical marijuana researchers can potentially learn some lessons from the experience of hydroxychloroquine researchers. Perhaps none is more important than the notion that researchers and regulators should only accept results from large-scale RCTs as evidence of a drug’s safety and efficacy regardless of political pressure or competing findings from other forms of research. Much of the harms related to hydroxychloroquine could have been averted if physicians and researchers insisted on proof of safety and efficacy from large-scale RCTs and if the FDA had imposed greater restrictions on use related to non-approved indications. Similarly, an insistence on large-scale RCTs to confirm the safety and efficacy of cannabis and cannabis-derived products, as well as stricter regulatory controls on unsubstantiated health claims made by marijuana marketers, could avert potential public health harms related to inappropriate medical cannabis use. Further, to the extent that cannabis and cannabis-derived products are truly safe and effective for certain conditions, large-scale RCTs can confirm these benefits and give policymakers, physicians, and patients the confidence to allocate appropriate treatment.To the layperson, other forms of research can appear to have equivalent or even greater value compared to large-scale RCTs . This is particularly true for observational studies. Observational studies can have thousands more participants than even large RCTs. They often use complex-sounding statistical techniques, like propensity score matching or growth models, while RCTs are statistically straightforward. Observational studies involve “real users” as opposed to clinical study test subjects. While some are aware of the concept of confounding, many can be appeased by adjustment for confounders in the analytical rather than design phase of the study. Despite their veneer of credibility, observational studies have no causal interpretations and, instead, can easily provide biased effect estimates. Large-scale RCTs are the only method that can reliably provide causal estimates of an effect . Consider, for example, a treatment that has no effect. The treatment is tested in 100 different trials, each with 1000 participants. The relationship between the treatment and the outcome of interest is confounded by 20 variables , which could be, for example, gender, race, age, height, weight, and blood pressure.
For simplicity, assume that each confounder is randomly distributed around 0.4 for those not receiving the treatment and 0.6 for those receiving the treatment. The effect size of each confounder on the outcome ranges between 0.5 and 5 , with an equal probability of either increasing or decreasing the outcome. To avoid overfitting, I include a random error centered at 0 with a standard deviation of 10. Observational researchers rarely know all potential confounders or even have access to data on all known confounders. Assume, then, that the researcher knows and collects data on, on average, 30% of the confounders, and adjusts for all of them. Many researchers would consider an observational study with six confounders “well-controlled”, and yet it is reasonable that 10 continuous and 10 dichotomous variables confound a given relationship. However, if we simulate this circumstance, approximately 85 of the 100 trials would produce an estimate significantly different from 0, even though the treatment truly has no effect . Each of these 85 trials may be publishable in separate peer-reviewed journal publications, but none of them would be accurate. Indeed, if the research or publication process is biased in one direction, cannabis grow system it may appear that the literature consistently shows a relationship in that direction. Large-scale RCTs eliminate the dangers emerging from unknown confounders. Because participants are randomized to receive either the treatment or a control and because the sample size of both groups are large, all third variables, including known and unknown confounders, balance between groups.1 In other terms, it is not possible for potential outcomes to correlate with the treatment when the treatment is randomly assigned. In the above example, this is analogous to adjusting for all 20 known and unknown confounders. If we simulate that case, approximately 95 of the 100 trials produce results consistent with the treatment’s true effect size. Other study designs have related problems. Small-scale RCTs, for example, do not necessarily balance confounders or potential outcomes; without help from the law of large numbers, the different treatment arms can, by chance, be correlated with known and unknown confounders and potential outcomes . Many initial safety trials do not have a comparison group altogether, and so the effect can be confounded by time or disease progression. Animal models and preclinical studies frequently fail to produce comparable results in humans due to the immeasurable number of confounding biological systems . For these reasons, large-scale RCTs are almost always required for drug approval by regulatory bodies in developed countries around the world. It should be noted that, in the United States, the 21st Century Cures Act has allowed for some flexibility in the study design and statistical analyses of trials used to test new medical devices and drugs’ safety and efficacy. Even in these cases, however, FDA guidance on Bayesian analysis affirms the importance and necessity of random assignment to treatment and a sufficient sample in late-stage investigational new drug trials . It is true that, strictly speaking, large-scale RCTs are not the only way to establish causal evidence . For example, natural experiments, which exploit random or quasirandom assignment occurring in the real world, can have many of the same benefits as RCTs and potentially better generalizability.
However, true natural experiments, particularly for the use of pharmaceutical products, are rare. Further, when a natural experiment is found, one needs to be convinced that assignment to the treatment is truly random, or at least orthogonal to potential outcomes conditional on adjusting for observed confounders, before accepting the results as causal. In virtually all cases, that argument requires at least a small leap of faith . Consequently, large-scale RCTs are the only study design that can reliably produce causal evidence. Large-scale RCTs have another key benefit over observational, early clinical, or natural experiment designs: it is challenging for researchers to intentionally bias their studies to find favorable results. For both pharmaceutical and marijuana research, researchers often have a considerable interest – financial, ideological, or otherwise – in producing findings that suggest the drugs they test are safe and effective. Dishonest researchers may, for example, selectively choose which confounders to include in their models in order to find a spurious but statistically significant result . Large-scale RCTs essentially remove the option for researchers to act in this way. Potential outcomes are balanced through the randomization procedure, and so the researcher merely has to perform some simple and straightforward analytics in order to assess whether the drug had an effect or not. She cannot purposely introduce bias into her model by omitting a confounder. Simply put, with large-scale RCTs, there is little room for dishonest researchers to play statistical games with their data. It should be noted that not all large-scale RCTs are properly formulated or conducted to produce clinically meaningful results, and the mere presence of a study that brands itself as a large-scale RCT is insufficient to determine whether a drug is safe and effective or not . For example, in trials that are inappropriately conducted, randomized groups may differ in post-randomization experiences or randomization may not be properly generated at all.