Assessing risk communication in breast cancer: are continuous measures of patient knowledge better than categorical?

OBJECTIVE

To compare the performance of categorical and continuous measures of patient knowledge in the context of risk communication about breast cancer, in terms of statistical and clinical significance as well as efficiency.

METHODS

Twenty breast cancer patients provided estimates of 10-year mortality risk before and after their oncology visit. The oncologist reviewed risk estimates from Adjuvant!, a well-validated and commonly used prognostic model. Using the Adjuvant! estimates as a gold standard, we calculated how accurate the patient estimates were before and after the visit. We used three novel continuous measures of patient accuracy, the absolute bias, Brier, and Kullback-Leibler scores, and compared them to a categorical measure in terms of sensitivity to intervention effects. We also calculated the sample size required to replicate the primary study using the categorical and continuous measures, as a means of comparing efficiency.

RESULTS

In this sample, the Kullback-Leibler measure was most sensitive to the intervention effects (p=0.004), followed by Brier and absolute bias (both p=0.011), and finally the categorical measure (0.125). The sample size required to replicate the primary study was 18 for the Kullback-Leibler measure, 23 for absolute bias and Brier, and 37 for the categorical measure.

CONCLUSIONS

The continuous measures led to more efficient sample sizes and to rejection of the null hypothesis of no intervention effect. However, the difference in sensitivity of the continuous measures was not statistically significant, and the performance of the categorical measure depends on the researcher's categorical cutoff for accuracy. Continuous measures of patient accuracy may be more sensitive and efficient, while categorical measures may be more clinically relevant.

PRACTICE IMPLICATIONS

Researchers and others interested in assessing the accuracy of patient knowledge should weigh the trade-offs between clinical relevance and statistical significance while designing or evaluating risk communication studies.