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      Pembrolizumab As Second-Line Therapy in Patients With Advanced Hepatocellular Carcinoma in KEYNOTE-240: A Randomized, Double-Blind, Phase III Trial

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          Abstract

          PURPOSE

          Pembrolizumab demonstrated antitumor activity and safety in the phase II KEYNOTE-224 trial in previously treated patients with advanced hepatocellular carcinoma (HCC). KEYNOTE-240 evaluated the efficacy and safety of pembrolizumab in this population.

          PATIENTS AND METHODS

          This randomized, double-blind, phase III study was conducted at 119 medical centers in 27 countries. Eligible patients with advanced HCC, previously treated with sorafenib, were randomly assigned at a two-to-one ratio to receive pembrolizumab plus best supportive care (BSC) or placebo plus BSC. Primary end points were overall survival (OS) and progression-free survival (PFS; one-sided significance thresholds, P = .0174 [final analysis] and P = .002 [first interim analysis], respectively). Safety was assessed in all patients who received ≥ 1 dose of study drug.

          RESULTS

          Between May 31, 2016, and November 23, 2017, 413 patients were randomly assigned. As of January 2, 2019, median follow-up was 13.8 months for pembrolizumab and 10.6 months for placebo. Median OS was 13.9 months (95% CI, 11.6 to 16.0 months) for pembrolizumab versus 10.6 months (95% CI, 8.3 to 13.5 months) for placebo (hazard ratio [HR], 0.781; 95% CI, 0.611 to 0.998; P = .0238). Median PFS for pembrolizumab was 3.0 months (95% CI, 2.8 to 4.1 months) versus 2.8 months (95% CI, 2.5 to 4.1 months) for placebo at the first interim analysis (HR, 0.775; 95% CI, 0.609 to 0.987; P = .0186) and 3.0 months (95% CI, 2.8 to 4.1 months) versus 2.8 months (95% CI, 1.6 to 3.0 months) at final analysis (HR, 0.718; 95% CI, 0.570 to 0.904; P = .0022). Grade 3 or higher adverse events occurred in 147 (52.7%) and 62 patients (46.3%) for pembrolizumab versus placebo; those that were treatment related occurred in 52 (18.6%) and 10 patients (7.5%), respectively. No hepatitis C or B flares were identified.

          CONCLUSION

          In this study, OS and PFS did not reach statistical significance per specified criteria. The results are consistent with those of KEYNOTE-224, supporting a favorable risk-to-benefit ratio for pembrolizumab in this population.

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          Most cited references7

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          Comparative analysis of two rates

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            Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests

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              Adjusting for treatment switching in randomised controlled trials – A simulation study and a simplified two-stage method

              Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) - whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.
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                Author and article information

                Journal
                Journal of Clinical Oncology
                JCO
                American Society of Clinical Oncology (ASCO)
                0732-183X
                1527-7755
                January 20 2020
                January 20 2020
                : 38
                : 3
                : 193-202
                Affiliations
                [1 ]University of California, Los Angeles, Los Angeles, CA
                [2 ]Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
                [3 ]Lyon North Hospital, Lyon, France
                [4 ]Kindai University Faculty of Medicine, Osaka, Japan
                [5 ]Beaujon University Hospital, Assistance Publique–Hôpitaux de Paris, Clichy, France
                [6 ]Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
                [7 ]NN Blokhin National Medical Research Center of Oncology, Ministry of Health, Moscow, Russian Federation
                [8 ]Centre Eugène Marquis, Rennes, France
                [9 ]Taipei Veterans General Hospital, Taipei, Taiwan
                [10 ]Chiba University Graduate School of Medicine, Chiba, Japan
                [11 ]The University at Hong Kong, Hong Kong, People’s Republic of China
                [12 ]Pontificia Universidad Catolica de Chile, Santiago, Chile
                [13 ]State Key Laboratory of Translation Oncology, Sir YK Pao Centre for Cancer, The Chinese University of Hong Kong, Hong Kong, People’s Republic of China
                [14 ]Princess Margaret Cancer Centre and University of Toronto, Toronto, Ontario, Canada
                [15 ]Ospedale del Mare, Napoli, Italy
                [16 ]Merck, Kenilworth, NJ
                [17 ]Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
                [18 ]National Taiwan University Hospital and National Taiwan University Cancer Center, Taipei, Taiwan
                Article
                10.1200/JCO.19.01307
                31790344
                d13a68df-6ed6-4d07-abd4-317f22d4cc37
                © 2020
                History

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