Letters COMMENT & RESPONSE Assessing Metronomic Chemotherapy for Progressive Pediatric Solid Malignant Tumors To the Editor In the trial conducted by Pramanik and colleagues 1 for treating progressive pediatric solid malignant tumors, the primary end point was progression-free survival (PFS). The jhazard ratio (HR) for metronomic chemotherapy vs best supportive care (BSC) was 0.69 (95% CI, 0.47-1.03; P = .07). Nu- merically, this HR value is impressive, but it is not statisti- cally significant. Therefore, the authors concluded that met- ronomic chemotherapy would not improve PFS, compared with placebo, among pediatric patients with extracranial pro- gressive solid malignant tumors. The profile of the difference of the 2 Kaplan-Meier PFS curves in Figure 2 of the article for metronomic chemotherapy and BSC suggests that the propor- tional hazards model assumption is not plausible. That is, the HR is not constant over the entire study follow-up time. This implies that the observed HR is difficult to interpret clinically and, furthermore, it is likely that the test based on HR might not have enough statistical power to detect a real metro- nomic treatment effect. The authors also reported that the me- dian PFS times between the 2 arms were similar. On the other hand, visually the Kaplan-Meier curves suggested that met- ronomic chemotherapy appeared to prolong the patients’ PFS after 2 months of follow-up. For this situation, the median is not sensitive enough to capture the relatively long-term sur- vival profile from metronomic chemotherapy. It is also known that generally the estimate of the median survival time is no- toriously unstable and results in an inconclusive claim about the treatment difference. An alternative approach to handle this case is to use the mean PFS time to quantify the treatment benefit, which can be estimated by the area under the Kaplan-Meier curve. 2-4 With reconstructed PFS data by scanning the Kaplan-Meier curves of Figure 2, 5 the estimated mean PFS times for metronomic chemotherapy and BSC are 2.4 and 1.6 months, respectively. The difference in the mean PFS time is 0.8 months (95% CI, 0.14-1.5 months; P = .02). This quantification results in a sta- tistically significant difference in favor of metronomic che- motherapy. Moreover, the difference of 0.8 months, coupled with the mean PFS time of 1.6 months for the control BSC arm, provides a more clinically meaningful interpretation of the treatment effect of metronomic chemotherapy than an HR of 0.65. The design and analysis of a future cancer clinical trial with overall survival/PFS outcome may be improved by adopt- ing a robust, efficient statistical procedure that enables clini- cally meaningful interpretation of the treatment effect. Xuemin Fang, PhD Hajime Uno, PhD Lee-Jen Wei, PhD Author Affiliations: Department of Clinical Medicine (Biostatistics), Kitasato University, Tokyo, Japan (Fang); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts (Uno); Department of Biostatistics, Harvard University, Boston, Massachusetts (Wei). Corresponding Author: Lee-Jen Wei, PhD, Department of Biostatistics, Harvard University, 655 Huntington Ave, Boston, MA 02115 (wei@hsph.harvard.edu). Published Online: January 25, 2018. doi:10.1001/jamaoncol.2017.3983 Conflict of Interest Disclosures: None reported. 1. Pramanik R, Agarwala S, Gupta YK, et al. Metronomic chemotherapy vs best supportive care in progressive pediatric solid malignant tumors: a randomized clinical trial. JAMA Oncol. 2017;3(9):1222-1227 . 2. Uno H, Claggett B, Tian L, et al. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol. 2014;32(22):2380-2385. 3. Uno H, Wittes J, Fu H, et al. Alternatives to hazard ratios for comparing the efficacy or safety of therapies in noninferiority studies. Ann Intern Med. 2015; 163(2):127-134. 4. Péron J, Roy P, Ozenne B, Roche L, Buyse M. The net chance of a longer survival as a patient-oriented measure of treatment benefit in randomized clinical trials. JAMA Oncol. 2016;2(7):901-905. 5. Guyot P, Ades AE, Ouwens MJ, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2012;12:9. jamaoncology.com (Reprinted) JAMA Oncology Published online January 25, 2018 E1 © 2018 American Medical Association. All rights reserved. Downloaded From: by a BSR-Univ degli Studi di Sassaro User on 01/30/2018