UCSF

Objective

Because each patient's baseline (pre-treatment) characteristics differ (e.g., age, sex, socioeconomic status, ethnicity/race, biomarkers), treatments do not work the same for every patient-some can even cause detrimental effects. To improve patient care, it is critical to identify such heterogeneity of treatment effects. But the standard analytic approach dichotomizes baseline characteristics (low vs. high) which often leads to a loss of critical patient-care information and power to detect heterogeneity, as the results may depend strongly on the cut-points chosen. A more powerful analytic approach is to analyze baseline characteristics (i.e., covariates) measured on a continuous scale that retains all of the information available for the covariate.

Methods

In this article, we show how the Johnson-Neyman (J-N) method can be used to identify the prognostic and predictive value of baseline covariates measured on a continuous scale - findings that often cannot be determined using the traditional dichotomized approach. As an example, we used the J-N method to explore treatment effects for varying levels of the biomarker salivary mutans streptococci (MS) in a randomized clinical prevention trial comparing fluoride varnish with no fluoride varnish for 376 initially caries-free high-risk children, all of whom received oral health counseling.

Results

The J-N analysis showed that children with higher baseline MS values who were randomized to receive fluoride varnish had the poorest dental caries prognosis and may have benefitted most from the preventive agent.

Conclusion

Such methods are likely to be an important tool in the field of personalized oral health care.