Combining Survival and Toxicity Effect Sizes from Clinical Trials: NCCTG 89-20-52 (Alliance) - Pages 137-146

Brittny T. Major-Elechi, Paul J. Novotny, Jasvinder A. Singh, James A. Bonner, Amylou C. Dueck, Daniel J. Sargent, Axel Grothey and Jeff A. Sloan

https://doi.org/10.6000/1929-6029.2018.07.04.4

Published: 16 November 2018

Abstract: Background: How can a clinician and patient incorporate survival and toxicity information into a single expression of comparative treatment benefit? Sloan et al. recently extended the ½ standard deviation concept for judging the clinical importance of findings from clinical trials to survival and tumor response endpoints. A new method using this approach to combine survival and toxicity effect sizes from clinical trials into a quality-adjusted effect size is presented.

Methods: The quality-adjusted survival effect size (QASES) is calculated as survival effect size (ESS) minus the calibrated toxicity effect sizes (EST) (QASES=ESS-EST). This combined effect size can be weighted to adjust for the relative emphasis placed by the patient on survival and toxicity effects.

Results: As an example, consider clinical trial NCCTG 89-20-52 which randomized patients to once-daily thoracic radiotherapy (ODTRT) versus twice-daily treatment of thoracic radiotherapy (TDRT) for the treatment of lung cancer. The ODTRT vs. TDRT arms had median survival time of 22 vs. 20 months (p=0.49) and toxicity rate of 39% vs. 54%, (p<0.05). The QASES of 0.18 standard deviations translates to a quality-adjusted survival difference of 5.7 months advantage for the ODRT arm over the TDRT treatment arm (22(16.3) months), p<0.05). Similar results are presented for the four possible case combinations of significant/non-significant survival and toxicity benefits using completed clinical trials.

Conclusions: We used a novel approach to re-analyze clinical trial data to produce a single estimate for each treatment that combines survival and toxicity data. The QASES approach is an intuitive and mathematically simple yet robust approach.

Keywords: Survival, toxicity, quality of life, effect size, quality-adjusted life years, QALY.

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