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In treatment or prevention of COVID-19, ivermectin is not approved by the United States (US) Food and Drug Administration (FDA). Nonetheless, in the US, prescriptions of ivermectin by healthcare providers have increased > tenfold from 3589 per week pre-COVID-19 to 39,102. Ivermectin is FDA approved for animals to treat parasites and for humans to treat intestinal strongyloidiasis and onchocerciasis orally, and ectoparasites and skin conditions topically. It is not a benign drug, with reported side effects including cutaneous, gastrointestinal, and cardiovascular symptoms. The evidence to support ivermectin to treat or prevent COVID-19 includes some basic research and inconsistent clinical observations that contribute to the formulation of a hypothesis of efficacy in COVID-19. At present, data from peer-reviewed published randomized trials of sufficient size, dose, and duration to reliably test the hypothesis of the most plausible small to moderate benefits on clinically relevant endpoints are sparse. In addition to the US FDA, the US National Institutes of Health, World Health Organization, and European Medicines Agency have all advised against ivermectin for treatment or prevention of COVID-19 outside of randomized trials. For ivermectin in treatment or prevention of COVID-19, healthcare providers should reassure all patients that if sufficient evidence were to emerge, then this drug could be considered a therapeutic innovation and regulatory authorities would approve the drug. In the meanwhile, we strongly recommend a moratorium on the prescription of ivermectin for the treatment or prevention of COVID-19 except in randomized trials to provide the most reliable test of the hypothesis.

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Policy makers require estimates of comparative effectiveness that apply to the population of interest, but there has been little research on quantitative approaches to assess and extend the generalizability of randomized controlled trial (RCT)-based evaluations. We illustrate an approach using observational data.
Our example is the Whole Systems Demonstrator (WSD) trial, in which 3230 adults with chronic conditions were assigned to receive telehealth or usual care. First, we used novel placebo tests to assess whether outcomes were similar between the RCT control group and a matched subset of nonparticipants who received usual care. We matched on 65 baseline variables obtained from the electronic medical record. Second, we conducted sensitivity analysis to consider whether the estimates of treatment effectiveness were robust to alternative assumptions about whether \"usual care\" is defined by the RCT control group or nonparticipants. Thus, we provided alternative estimates of comparative effectiveness by contrasting the outcomes of the RCT telehealth group and matched nonparticipants.
For some endpoints, such as the number of outpatient attendances, the placebo tests passed, and the effectiveness estimates were robust to the choice of comparison group. However, for other endpoints, such as emergency admissions, the placebo tests failed and the estimates of treatment effect differed markedly according to whether telehealth patients were compared with RCT controls or matched nonparticipants.
The proposed placebo tests indicate those cases when estimates from RCTs do not generalize to routine clinical practice and motivate complementary estimates of comparative effectiveness that use observational data. Future RCTs are recommended to incorporate these placebo tests and the accompanying sensitivity analyses to enhance their relevance to policy making.

The National Lung Cancer Screening Trial (NLST) demonstrated a mortality reduction benefit associated with low-dose computed tomography (LDCT) screening for lung cancer. There has been considerable debate regarding the benefits and harms of LDCT lung cancer screening, including the challenges related to its practical implementation. One of the controversies regards overdiagnosis, which conceptually denotes diagnosing a cancer that, either because of its indolent, low-aggressiveness biologic behavior or because of limited life expectancy, is unlikely to result in significant morbidity during the patient\'s remainder lifetime. In theory, diagnosing and treating these cancers offer no measurable benefit while incurring costs and risks. Therefore, if a screening test detects a substantial number of overdiagnosed cancers, it is less likely to be effective. It has been argued that LDCT screening for lung cancer results in an unacceptably high rate of overdiagnosis. This article aims to defend the opposite stance. Overdiagnosis does exist and to a certain extent is inherent to any cancer-screening test. Nonetheless, the concept is less dualistic and more nuanced than it has been suggested. Furthermore, the average estimates of overdiagnosis in LDCT lung cancer screening based on the totality of published data are likely much lower than the highest published estimates, if a careful definition of a positive screening test reflecting our current understanding of lung cancer biology is utilized. This article presents evidence on why reports of overdiagnosis in lung cancer screening have been exaggerated.