Estimating Subject-Specific Treatment Differences for Risk-Benefit Assessment with Applications to Beta-Blocker Effectiveness Trials

In the recent past, several clinical trials have sought to evaluate the effectiveness of beta-blocking drugs in patients with chronic heart failure. Although the studies of certain drugs in this class yielded overwhelmingly positive results, other studies resulted in a much less clear interpretation. As result, attention has appropriately been placed on the impact of patient heterogeneity on treatment assessment. For clinical practice, it is desirable to identify subjects who would benefit from the new treatment from a risk-benefit perspective. In this paper, we investigate the results of the noted Beta-Blocker Evaluation of Survival Trial (BEST) and implement a systematic approach to achieve this goal by analyzing data available early in the study, at the time of a hypothetical initial interim analysis. We utilize multinomial outcome data from these initial patients to build a parametric score for the purpose of stratifying the remaining patients in the BEST study. We then use the data from the remaining BEST study participants to obtain a nonparametric estimate of the treatment effects, with respect to each of several ordered patient outcomes that encompass both risks and benefits of treatment, for any fixed score. Furthermore, confidence interval and band estimates are constructed to quantify the uncertainty of our inferences for the treatment differences over a range of scores. We indeed detect subsets of patients who experience significant treatment benefits in addition to other patient groups who appear to be poor candidates for treatment.