What studies should I believe and how do I understand them?!
As we make decisions about health and wellness it is important to know what the research says, while remembering that a heart-wrenching story is not proof. By looking at scientific evidence we can determine the weight to place on a particular lifestyle change or intervention, with the hope of avoiding having too big or too small of a reaction. Understanding what the key terms mean will give us the opportunity for improved health by helping us make smart decisions without overreacting.
“People who are too afraid of relatively small risks, or too unafraid of big ones, make dangerous choices.”
warning! Risks are not Causes:
Photo by Goh Rhy Yan on Unsplash
A risk simply means there is an association between two things. In Minnesota, football season and snow are associated, but one does not cause the other! With health related risks, a study might find an association that could in the end just be a coincidence. Only truly big risks can be considered causes.
Randomized Controlled Trial:
Look for randomized controlled trials! RCTs are the most meaningful study type for determining the benefit or harm of an intervention, because they seek to rule out the possibility the observed association was caused by a third factor linked to both intervention and outcome. RCTs randomly assigns participants into an experimental group or a control group with both the clinician and participant being unaware of the assignment. This reduces the chance of an exaggerated estimate of the treatments impact. Although no study can be used to 100% identify the cause of an outcome, RCTs can find associations with results that can be statistically analyzed with identified levels of confidence. Randomized controlled trials are the most rigorous way of determining whether there is a cause-effect relationship between a given behavior and the outcome. More on types of studies here.
frequency of negative results:
Imagine that a randomized controlled trial of the elderly with a particular disease found that 30% of the control group developed something bad, compared with only 15% of those receiving the intervention. Should we agree to encourage grandma try the intervention?
Absolute Risk Reduction (ARR):
Our absolute risk (AR) is calculated by risk factors and age, and describes our chance of developing a disease over a specific period of time. Such as, a woman’s absolute lifetime risk of developing breast cancer is 1 in 8 or 12.5%. In the above example, the ARR is 15% (30% - 15% = 15%). Meaning that, if 100 elderly were treated, 15 would be prevented from developing bad outcomes.
Relative Risk (RR):
Relative risk compares differences between groups of individuals, and can be confusing. The RR of a bad outcome in a group given an intervention is a measure estimating the size of the effect of a treatment compared with other interventions or no treatment at all. In our case, the RR is 0.5 (15% ÷ 30% = 0.5). One is the baseline, a RR greater than 1 shows an increase in a bad outcome, while anything less than 1 shows a decrease. For example, when the RR is 2.0 the chance of a bad outcome is twice as likely to occur with the treatment as without it, whereas an RR of 0.5 means that the chance of a bad outcome is twice as likely to occur without the intervention.
“If 9 of the 10 pairs of sneakers that were worn without socks were smelly, and 2 of the 10 pairs of sneakers worn with socks were smelly. The relative risk would be (9/10) / (2/10), or 4.5. Therefore, the data suggest it is four times more likely to have smelly shoes if shoes are worn without socks.”
Relative Risk Reduction (RRR):
The RRR tells you by how much the treatment reduced the risk of bad outcomes when compared to the control group who did not have the treatment. In the previous example, the relative risk reduction of the bad outcome in the group of the elderly on the intervention was 50% (1 – 0.5 = 0.5 or 50%).
Example:
The ARR is largest in those at greatest risk; the greater your risk the more you stand to gain from the intervention. Think of it this way, if a person’s absolute risk (AR) of an asthma attack is 0.25 without treatment but falls to 0.20 with treatment, the ARR is 25% – 20% = 5%. The RRR is (25% – 20%) / 25% = 20%. The NNT is 1 / 0.05 = 20.
In a person with an AR of an asthma attack of only 0.025 without treatment, the same treatment will still produce a 20% RRR, but treatment will reduce there AR of an asthma attack to 0.020, giving a much smaller ARR of 2.5% – 2% = 0.5%, and an NNT of 200.ARR is known to be the most useful way research represents results.
Number Needed to Treat (NNT):
With NNT, If 15 elderly out of 100 benefit from treatment, the NNT for one elder to benefit is about 7 (100 ÷ 15 = ).
Confidence Intervals (CIs):
CIs seek to give us an idea of how confident we can be about a study’s estimate of it’s effects. The narrower the range, the more confident we can be that it is a real finding not just chance. CI’s are typically expressed in terms of a 95% confidence interval (95% CI), which represents the range of results within which we can be 95% certain that the true answer lies. The size of the group studied is an important factor that impacts the CI!
by looking for double-blinded randomized control studies and having a clear understanding of what the key terms mean we will be in a position to make smart decisions!
Wendy Dellis is a certified wellness coach. She joins years of training and work in the area of behavior change, experience as a fitness instructor and run club coordinator with a passion for adventure and people. She lives in Minnesota with her husband, Jay, and two sons.
The information contained on this page is for general information purposes only. Nothing here should be construed as medical or healthcare advice, but only topics for discussion. No physician-patient relationship exists; please consult your physician before making changes in diet or lifestyle.