Caveats: Published data is mostly industry-sponsored and therefore may be disproportionately positive; negative data (showing no benefit) may exist and not be available or published. It is also notable and unfortunate that there are no data addressing mortality. It would be helpful to know if hospitalized patients with influenza infections are benefited, harmed, or unaffected by NI’s. Given the 30,000-40,000 in-hospital deaths that occur each year secondary to influenza it would be important to have these data, and the subject pool for randomized trials is certainly available. We worry that such a study will never occur; while we believe evidentiary equipoise exists it is unlikely that clinical equipoise does * . Plainly, the existing data seems equivocal (evidentiary equipoise) but clinical consensus (clinical equipoise) will likely strongly favor the use of NI’s making the ethics of a randomized controlled trial controversial.
There are numerous studies showing a small symptom benefit to NI’s. The benefit seems clinically insignificant which is why many current health policy guidelines do not recommend NI’s for otherwise healthy adults. There is no evidence to support NI's as an agent to reduce morbidy/mortality for any group of adults, healthy or otherwise. Use in the setting of a pandemic remains unstudied, though theoretically may be helpful to prevent the spread of influenza via the drug’s mechanism of action (decreased viral shedding from infected cells).
For more information on recent controversies and opinions please explore , where many recent publications have addressed the evidentiary base for NI’s, and also Tom Jefferson's (Acute Respiratory Infections Group, Cochrane Collaboration) blog post .
The number of treatment units (subjects or groups of subjects) assigned to control and treatment groups, affects an RCT's reliability. If the effect of the treatment is small, the number of treatment units in either group may be insufficient for rejecting the null hypothesis in the respective statistical test . The failure to reject the null hypothesis would imply that the treatment shows no statistically significant effect on the treated in a given test . But as the sample size increases, the same RCT may be able to demonstrate a significant effect of the treatment, even if this effect is small.