Understanding the Results of Randomized Controlled Trials: A Refresher

KATE ROWLAND; MARK EBELL

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Understanding the Results of Randomized Controlled Trials: A Refresher

KATE ROWLAND, MD

MARK EBELL, MD

FPM. 2026;33(2):27-31.

Author disclosures: no relevant financial relationships.

This content conforms to AAFP criteria for CME.

A few quick calculations can help you interpret and apply study results to improve patient care.

Decisions about screening, prevention, and treatment should ideally be based on the results of randomized controlled trials (RCTs) or systematic reviews of RCTs. However, these studies often require some analysis to determine what the results mean for individual patients.

In this article, we review how to interpret and apply different measures of benefit and harm, with a focus on absolute risks and the number needed to treat. We also discuss considerations such as statistical versus clinical significance, indication creep, and how to interpret composite outcomes that RCTs increasingly use.

KEY POINTS

Studies often require analysis to determine what the results mean for individual patients; a few simple calculations can help.
The absolute risk reduction (ARR) provides a more realistic, clinically relevant picture than the relative risk reduction, and the number needed to treat helps translate the ARR into a concrete, understandable number.
Consider whether all study outcomes improved similarly or whether an individual outcome drove the results, whether “indication creep” is occurring, and whether the results are clinically significant, not just statistically significant.

ABSOLUTE RISK REDUCTION AND NUMBER NEEDED TO TREAT

While studies often report relative risks, these can be misleading. A relative risk of 0.5 can describe a reduction in mortality from 50% to 25%, from 10% to 5%, or from 1% to 0.5%. Obviously, the magnitude of these results varies greatly. Therefore, when reading an RCT, you should always look for the absolute risk of the outcome (such as mortality) in each treatment group rather than relative risks.

For example, your 73-year-old patient, Fred, is concerned because he has some risk factors for heart disease, and after seeing several scary ads on TV, he asks whether he should take a new medicine called “CardioBoost.” Table 1 summarizes the results of a placebo-controlled RCT for this fictional cardiac medication. In this case, the study followed patients with established heart disease who were given the treatment or placebo for four years.

Outcome	CardioBoost (n=4890)	Placebo (n=4884)	Relative risk (95% CI)	p
Primary composite outcome^*	820 (16.8%)	987 (19.8%)	0.85 (0.80 to 0.90)	< 0.001
Death	220 (4.5%)	233 (4.8%)	0.94 (0.85 to 1.03)	0.123
Non-fatal stroke	180 (3.7%)	174 (3.6%)	1.03 (0.92 to 1.13)	0.754
Non-fatal myocardial infarction	316 (6.7%)	362 (7.4%)	0.91 (0.83 to 0.98)	0.065
Hospitalization for heart failure	104 (2.1%)	218 (4.5%)	0.47 (0.37 to 0.57)	< 0.001

*Combination of death, non-fatal stroke, non-fatal myocardial infarction, or hospitalization for heart failure.

Dr. Rowland is an associate professor in the Department of Family and Preventive Medicine, Rush Medical College, Chicago.

Dr. Ebell is a professor in the Department of Family Medicine, Michigan State University, East Lansing, Mich.

Author disclosures: no relevant financial relationships.

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