It's often hard to know what to make of the overwhelming claims made by marketers of herbal pills, nutritional supplements and enhanced food products. Deceptive ads showing photos of doctors with white lab coats looking through a microscope, accompanied by amazing weight loss claims, with no reliable, scientific evidence provided, is just one example. The consumer is indirectly and deviously given the impression the miracle product was rigorously, scientifically tested by medical researchers. There are similar ads for vitamin supplements, infant formulas, nasal strips and herbal supplements. This is just the tip of the iceberg in the lawless, wild, wild west of health advertising.
When deciding whether to purchase a health product with exaggerated claims and from a dubious source, inquire if these 5 basic, essential research criteria were used. The thousands of snake-oil, quack medicine con artists out there will fall short in all five areas. But wait - it's a conspiracy! Below is an excellent summary of the five criteria from the US Federal Trade Commission.
Control Group
"Human clinical studies should have both a treatment group and a control group. The efficacy of a product should be demonstrated by comparing the results of the treatment group to the results of the control group. Improvements over time in the treatment group alone could result from a placebo effect, spontaneous changes in subjects’ health, improvements in performance on a test measure purely as the result of practice or repetition (the 'practice effect'), or other variables unrelated to the product’s benefits.
"An appropriately designed control (ideally a control using a placebo or sham treatment) helps to isolate the effects of these other variables from the effect of the treatment. When studies employ a cross-over design, in which subjects serve as their own control, they should use a sufficient wash-out period (the period during which subjects don’t receive the treatment) to ensure clarity as to what is causing the observed results. A cross-over design may not be appropriate to test some hypotheses."
Randomization
"The study should use appropriate randomization or, in the alternative, careful matching criteria, to prevent selection bias and to assure that demographic characteristics and other variables are similar in the control group and the treatment group. Substantial differences between the control and treatment groups in age, gender, diet, health status, or other characteristics can undermine the validity of any findings."
Double Blinding
"Both the participants in a study and the researchers should be blinded as to who is in the treatment group and who is in the control group. This greatly reduces the likelihood that either the subjects or the researchers might consciously or unconsciously take actions potentially biasing the results. In the rare circumstances where a double-blind design isn’t feasible, the study should be blinded to the fullest extent possible and researchers should take steps to minimize any potential for bias."
Statistically Significant Results
"To support a health-related claim, human clinical research must yield results that are statistically significant. A study that fails to show a statistically significant difference between the treatment and control groups may indicate that the measured effect is merely the result of placebo effect, unrelated improvement over time, or chance. Studies that use multiple outcome measures should report all outcomes, rather than selectively reporting positive outcomes. Such studies also should include a statistical adjustment to account for the increased likelihood that, when multiple outcomes are measured, a positive result on any one of the measures may be due to chance.
"In addition, a post hoc analysis of data – one that departs from the original study protocol – can be an indication that the researchers are engaging in data mining or 'p-hacking' in an attempt to find some positive result to report from a study that otherwise failed to show any treatment effect. The more post hoc comparisons examined, the more likely the data will yield a significant difference that is merely the result of chance. For that reason, post hoc analysis that departs from the originally stated study protocol (e.g., an analysis that looks at various smaller subgroups of the study population) may identify areas for future exploration, but doesn’t generally provide reliable evidence to substantiate a claim."
Clinically Meaningful Results
"Any statistically significant results must translate to a benefit that is clinically meaningful for consumers. Some results that are statistically significant may be too small to provide real consequences for consumer health. Studies that fail to satisfy these basic principles are more prone to bias and other confounding factors, unlikely to yield reliable results, and generally won’t meet the FTC’s competent and reliable scientific evidence standard for substantiating health-related claims."
Source Products Compliance Guidance (includes 52 examples) https://www.ftc.gov/business-guidance/resources/health-products-compliance-guidance
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Jerry De Luca is a Christian freelance writer who loves perusing dozens of interesting and informative publications. When he finds any useful info he summarizes it, taking the main points, and creates a (hopefully) helpful blog post.
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