Bayesian additional evidence for decision making under small sample uncertainty

Background Statistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes. Methods We developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distribution is not needed; instead, the tipping point output is compared to reasonable effect ranges to draw conclusions. We demonstrate our approach in a comparative effectiveness analysis comparing two treatments in a real world biomarker-defined cohort, and provide guidelines for how to apply BAE in practice. Results Our initial comparative effectiveness analysis results in a hazard ratio of 0.31 with 95% confidence interval (0.09, 1.1). Applying BAE to this result yields a tipping point of 0.54; thus, an observed hazard ratio of 0.54 or smaller in a replication study would result in posterior credibility for the treatment association. Given that effect sizes in this range are not extreme, and that supportive evidence exists from a similar published study, we conclude that this problem is worthy of further research. Conclusions Our proposed method provides a useful framework for interpreting analytic results from small datasets. This can assist researchers in deciding how to interpret and continue their investigations based on an initial analysis that has high uncertainty. Although we illustrated its use in estimating parameters based on time-to-event outcomes, BAE easily applies to any normally-distributed estimator, such as those used for analyzing binary or continuous outcomes.

Comparative effectiveness analysis between entrectinib clinical trial and crizotinib real-world data in ROS1+ NSCLC

Aim: Generating direct comparative evidence in prospective randomized trials is difficult for rare diseases. Real-world cohorts may supplement control populations. Methods: Entrectinib-treated adults with advanced ROS1 fusion-positive NSCLC (n = 94) from Phase I/II trials (ALKA-372-001 [EudraCT2012-00148-88], STARTRK-1 [NCT02097810], and STARTRK-2 [NCT02568267]) were compared with a real-world crizotinib-treated cohort (n = 65). Primary end point, time-to-treatment discontinuation (TTD); secondary end points, PFS and OS. Results: Median (95% CI) weighted TTD: 12.9 (9.9–17.4) months for entrectinib; 8.8 (6.2–9.9) months for crizotinib (weighted hazard ratio, 0.72 [0.51–1.02]). Median OS with entrectinib was not reached, weighted median OS with crizotinib was 18.5 (15.1–19.9) months. Conclusion: Entrectinib administered in clinical trials may be associated with longer TTD than a real-world crizotinib population.