Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Assessing Treatment Benefit in Immuno-oncology

  • 18 Accesses

Abstract

Immuno-oncology is a buoyant field of research, with recently developed drugs showing unprecedented response rates and/or a hope for a meaningful prolongation of the overall survival of some patients. These promising clinical developments have also pointed to the need of adapting statistical methods to best describe and test for treatment effects in randomized clinical trials. We review adaptations to tumor response and progression criteria for immune therapies. Survival may be the endpoint of choice for clinical trials in some tumor types, and the search for surrogate endpoints is likely to continue to try and reduce the duration and size of clinical trials. In situations for which hazards are likely to be non-proportional, weighted logrank tests may be preferred as they have substantially more power to detect late separation of survival curves. Alternatively, there is currently much interest in accelerated failure time models, and in capturing treatment effect by the difference in restricted mean survival times between randomized groups. Finally, generalized pairwise comparisons offer much promise in the field of immuno-oncology, both to detect late emerging treatment effects and as a general approach to personalize treatment choices through a benefit/risk approach.

This is a preview of subscription content, log in to check access.

Fig. 1

References

  1. 1.

    Hoos A, Britten C (2012) The immuno-oncology framework: enabling a new era of cancer therapy. Oncoimmunology 1:334–339

  2. 2.

    Hoos A (2016) Development of immuno-oncology drugs—from CTLA4 to PD1 to the next generations. Nat Rev Drug Discov 15:235–247

  3. 3.

    Topalian SL, Weiner GJ, Pardoll DM (2011) Cancer immunotherapy comes of age. J Clin Oncol 29:4828–4836

  4. 4.

    Tsimberidou AM, Levit LA, Schilsky RL, Averbuch SD, Chen D, Kirkwood JM, McShane LM, Sharon E, Mileham KF, Postow MA (2019) Trial reporting in immuno-oncology (TRIO): an American Society of clinical oncology-society for immunotherapy of cancer statement. J Clin Oncol 37:72–80

  5. 5.

    Tang J, Shalabi A, Hubbard-Lucey VM (2018) Comprehensive analysis of the clinical immuno-oncology landscape. Ann Oncol 29:84–91

  6. 6.

    Anagnostou V, Yarchoan M, Hansen AR, Wang H, Verde F, Sharon E, Collyar D, Chow LQM, Forde PM (2017) Immuno-oncology trial endpoints: capturing clinically meaningful activity. Clin Cancer Res 23:4959–4969

  7. 7.

    Borcoman E, Kanjanapan Y, Champiat S, Kato S, Servois V, Kurzrock R, Goel S, Bedard P, Le Tourneau C (2019) Novel patterns of response under immunotherapy. Ann Oncol 30:385–396

  8. 8.

    Chen TT (2013) Statistical issues and challenges in immuno-oncology. J Immunother Cancer 1:18

  9. 9.

    Hales RK, Banchereau J, Ribas A, Tarhini AA, Weber JS, Fox BA, Drake CG (2010) Assessing oncologic benefit in clinical trials of immunotherapy agents. Ann Oncol 21:1944–1951

  10. 10.

    Hoos A, Eggermont AM, Janetzki S, Hodi FS, Ibrahim R, Anderson A, Humphrey R, Blumenstein B, Old L, Wolchok J (2010) Improved endpoints for cancer immunotherapy trials. J Natl Cancer Inst 102:1388–1397

  11. 11.

    Huang B (2018) Some statistical considerations in the clinical development of cancer immunotherapies. Pharm Stat 17:49–60

  12. 12.

    Finn OJ (2012) Immuno-oncology: understanding the function and dysfunction of the immune system in cancer. Ann Oncol 23(Suppl 8):viii6–viii9

  13. 13.

    Cogdill AP, Andrews MC, Wargo JA (2017) Hallmarks of response to immune checkpoint blockade. Br J Cancer 117:1–7

  14. 14.

    Daud AI, Loo K, Pauli ML, Sanchez-Rodriguez R, Sandoval PM, Taravati K, Tsai K, Nosrati A, Nardo L, Alvarado MD et al (2016) Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J Clin Invest 126:3447–3452

  15. 15.

    Mittal D, Gubin MM, Schreiber RD, Smyth MJ (2014) New insights into cancer immunoediting and its three component phases–elimination, equilibrium and escape. Curr Opin Immunol 27:16–25

  16. 16.

    Ritchie G, Gasper H, Man J, Lord S, Marschner I, Friedlander M, Lee CK (2018) Defining the most appropriate primary end point in phase 2 trials of immune checkpoint inhibitors for advanced solid cancers: a systematic review and meta-analysis. JAMA Oncol 4:522–528

  17. 17.

    Chiou VL, Burotto M (2015) Pseudoprogression and immune-related response in solid tumors. J Clin Oncol 33:3541–3543

  18. 18.

    Tazdait M, Mezquita L, Lahmar J, Ferrara R, Bidault F, Ammari S, Balleyguier C, Planchard D, Gazzah A, Soria JC et al (2018) Patterns of responses in metastatic NSCLC during PD-1 or PDL-1 inhibitor therapy: comparison of RECIST 1.1, irRECIST and iRECIST criteria. Eur J Cancer 88:38–47

  19. 19.

    Wolchok JD, Hoos A, O’Day S, Weber JS, Hamid O, Lebbe C, Maio M, Binder M, Bohnsack O, Nichol G et al (2009) Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 15:7412–7420

  20. 20.

    Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, Chmielowski B, Spasic M, Henry G, Ciobanu V et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515:568–571

  21. 21.

    Hodi FS, Ballinger M, Lyons B, Soria JC, Nishino M, Tabernero J, Powles T, Smith D, Hoos A, McKenna C et al (2018) Immune-modified response evaluation criteria in solid tumors (imRECIST): refining guidelines to assess the clinical benefit of cancer immunotherapy. J Clin Oncol 36:850–858

  22. 22.

    Hodi FS, Hwu WJ, Kefford R, Weber JS, Daud A, Hamid O, Patnaik A, Ribas A, Robert C, Gangadhar TC et al (2016) Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with pembrolizumab. J Clin Oncol 34:1510–1517

  23. 23.

    Champiat S, Dercle L, Ammari S, Massard C, Hollebecque A, Postel-Vinay S, Chaput N, Eggermont A, Marabelle A, Soria JC et al (2017) Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin Cancer Res 23:1920–1928

  24. 24.

    Ferrara R, Mezquita L, Texier M, Lahmar J, Audigier-Valette C, Tessonnier L, Mazieres J, Zalcman G, Brosseau S, Le Moulec S et al (2018) Hyperprogressive disease in patients with advanced non-small cell lung cancer treated With PD-1/PD-L1 inhibitors or with single-agent chemotherapy. JAMA Oncol 4:1543–1552

  25. 25.

    Saada-Bouzid E, Defaucheux C, Karabajakian A, Coloma VP, Servois V, Paoletti X, Even C, Fayette J, Guigay J, Loirat D et al (2017) Hyperprogression during anti-PD-1/PD-L1 therapy in patients with recurrent and/or metastatic head and neck squamous cell carcinoma. Ann Oncol 28:1605–1611

  26. 26.

    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

  27. 27.

    WHO (1979) WHO handbook for reporting results of cancer treatment. World Health Organization Offset Publication No. 48, Geneva

  28. 28.

    Nishino M, Giobbie-Hurder A, Gargano M, Suda M, Ramaiya NH, Hodi FS (2013) Developing a common language for tumor response to immunotherapy: immune-related response criteria using unidimensional measurements. Clin Cancer Res 19:3936–3943

  29. 29.

    Seymour L, Bogaerts J, Perrone A, Ford R, Schwartz LH, Mandrekar S, Lin NU, Litiere S, Dancey J, Chen A et al (2017) iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol 18:e143–e152

  30. 30.

    Chmielowski B (2018) How should we assess benefit in patients receiving checkpoint inhibitor therapy? J Clin Oncol 36:835–836

  31. 31.

    Kazandjian D, Keegan P, Suzman DL, Pazdur R, Blumenthal GM (2017) Characterization of outcomes in patients with metastatic non-small cell lung cancer treated with programmed cell death protein 1 inhibitors past RECIST version 1.1-defined disease progression in clinical trials. Semin Oncol 44:3–7

  32. 32.

    Escudier B, Motzer RJ, Sharma P, Wagstaff J, Plimack ER, Hammers HJ, Donskov F, Gurney H, Sosman JA, Zalewski PG et al (2017) Treatment beyond progression in patients with advanced renal cell carcinoma treated with nivolumab in CheckMate 025. Eur Urol 72:368–376

  33. 33.

    George S, Motzer RJ, Hammers HJ, Redman BG, Kuzel TM, Tykodi SS, Plimack ER, Jiang J, Waxman IM, Rini BI (2016) Safety and efficacy of nivolumab in patients with metastatic renal cell carcinoma treated beyond progression: a subgroup analysis of a randomized clinical trial. JAMA Oncol 2:1179–1186

  34. 34.

    Long GV, Weber JS, Larkin J, Atkinson V, Grob JJ, Schadendorf D, Dummer R, Robert C, Marquez-Rodas I, McNeil C et al (2017) Nivolumab for patients with advanced melanoma treated beyond progression: analysis of 2 phase 3 clinical trials. JAMA Oncol 3:1511–1519

  35. 35.

    Cottrell TR, Thompson ED, Forde PM, Stein JE, Duffield AS, Anagnostou V, Rekhtman N, Anders RA, Cuda JD, Illei PB et al (2018) Pathologic features of response to neoadjuvant anti-PD-1 in resected non-small-cell lung carcinoma: a proposal for quantitative immune-related pathologic response criteria (irPRC). Ann Oncol 29:1853–1860

  36. 36.

    Stein JE, Soni A, Danilova L, Cottrell TR, Gajewski TF, Hodi FS, Bhatia S, Urba WJ, Sharfman WH, Wind-Rotolo M et al (2019) Major pathologic response on biopsy (MPRbx) in patients with advanced melanoma treated with anti-PD-1: evidence for an early, on-therapy biomarker of response. Ann Oncol 30:589–596

  37. 37.

    Beaver JA, Howie LJ, Pelosof L, Kim T, Liu J, Goldberg KB, Sridhara R, Blumenthal GM, Farrell AT, Keegan P et al (2018) A 25-year experience of US Food and Drug Administration accelerated approval of malignant hematology and oncology drugs and biologics: a review. JAMA Oncol 4:849–856

  38. 38.

    Gettinger S, Horn L, Jackman D, Spigel D, Antonia S, Hellmann M, Powderly J, Heist R, Sequist LV, Smith DC et al (2018) Five-year follow-up of nivolumab in previously treated advanced non-small-cell lung cancer: results From the CA209-003 study. J Clin Oncol 36:1675–1684

  39. 39.

    Maio M, Grob JJ, Aamdal S, Bondarenko I, Robert C, Thomas L, Garbe C, Chiarion-Sileni V, Testori A, Chen TT et al (2015) Five-year survival rates for treatment-naive patients with advanced melanoma who received ipilimumab plus dacarbazine in a phase III trial. J Clin Oncol 33:1191–1196

  40. 40.

    Topalian SL, Sznol M, McDermott DF, Kluger HM, Carvajal RD, Sharfman WH, Brahmer JR, Lawrence DP, Atkins MB, Powderly JD et al (2014) Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J Clin Oncol 32:1020–1030

  41. 41.

    Attia P, Phan GQ, Maker AV, Robinson MR, Quezado MM, Yang JC, Sherry RM, Topalian SL, Kammula US, Royal RE et al (2005) Autoimmunity correlates with tumor regression in patients with metastatic melanoma treated with anti-cytotoxic T-lymphocyte antigen-4. J Clin Oncol 23:6043–6053

  42. 42.

    Maude SL, Frey N, Shaw PA, Aplenc R, Barrett DM, Bunin NJ, Chew A, Gonzalez VE, Zheng Z, Lacey SF et al (2014) Chimeric antigen receptor T cells for sustained remissions in leukemia. N Engl J Med 371:1507–1517

  43. 43.

    Blumenthal GM, Pazdur R (2016) Response rate as an approval end point in oncology: back to the future. JAMA Oncol 2:780–781

  44. 44.

    Andtbacka RH, Kaufman HL, Collichio F, Amatruda T, Senzer N, Chesney J, Delman KA, Spitler LE, Puzanov I, Agarwala SS et al (2015) Talimogene laherparepvec improves durable response rate in patients with advanced melanoma. J Clin Oncol 33:2780–2788

  45. 45.

    Morgan TM (1988) Analysis of duration of response: a problem of oncology trials. Control Clin Trials 9:11–18

  46. 46.

    Ellis S, Carroll KJ, Pemberton K (2008) Analysis of duration of response in oncology trials. Contemp Clin Trials 29:456–465

  47. 47.

    Huang B, Tian L, Talukder E, Rothenberg M, Kim DH, Wei LJ (2018) Evaluating treatment effect based on duration of response for a comparative oncology study. JAMA Oncol 4:874–876

  48. 48.

    Korn EL, Othus M, Chen T, Freidlin B (2017) Assessing treatment efficacy in the subset of responders in a randomized clinical trial. Ann Oncol 28:1640–1647

  49. 49.

    Blumenthal GM, Karuri SW, Zhang H, Zhang L, Khozin S, Kazandjian D, Tang S, Sridhara R, Keegan P, Pazdur R (2015) Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non-small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses. J Clin Oncol 33:1008–1014

  50. 50.

    Burzykowski T, Buyse M, Piccart-Gebhart MJ, Sledge G, Carmichael J, Luck HJ, Mackey JR, Nabholtz JM, Paridaens R, Biganzoli L et al (2008) Evaluation of tumor response, disease control, progression-free survival, and time to progression as potential surrogate end points in metastatic breast cancer. J Clin Oncol 26:1987–1992

  51. 51.

    Buyse M, Thirion P, Carlson RW, Burzykowski T, Molenberghs G, Piedbois P (2000) Relation between tumour response to first-line chemotherapy and survival in advanced colorectal cancer: a meta-analysis. Meta-analysis Group in Cancer. Lancet 356:373–378

  52. 52.

    Kaufman HL, Schwartz LH, William WN Jr, Sznol M, Fahrbach K, Xu Y, Masson E, Vergara-Silva A (2018) Evaluation of classical clinical endpoints as surrogates for overall survival in patients treated with immune checkpoint blockers: a systematic review and meta-analysis. J Cancer Res Clin Oncol 144:2245–2261

  53. 53.

    Roviello G, Andre F, Venturini S, Pistilli B, Curigliano G, Cristofanilli M, Rosellini P, Generali D (2017) Response rate as a potential surrogate for survival and efficacy in patients treated with novel immune checkpoint inhibitors: a meta-regression of randomised prospective studies. Eur J Cancer 86:257–265

  54. 54.

    Hamid O, Robert C, Daud A, Hodi FS, Hwu WJ, Kefford R, Wolchok JD, Hersey P, Joseph R, Weber JS et al (2019) Five-year survival outcomes for patients with advanced melanoma treated with pembrolizumab in KEYNOTE-001. Ann Oncol 30:501–503

  55. 55.

    Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, Gonzalez R, Robert C, Schadendorf D, Hassel JC et al (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 363:711–723

  56. 56.

    Mick R, Chen TT (2015) Statistical challenges in the design of late-stage cancer immunotherapy studies. Cancer Immunol Res 3:1292–1298

  57. 57.

    Saad ED, Buyse M (2016) Statistical controversies in clinical research: end points other than overall survival are vital for regulatory approval of anticancer agents. Ann Oncol 27:373–378

  58. 58.

    Kantoff PW, Higano CS, Shore ND, Berger ER, Small EJ, Penson DF, Redfern CH, Ferrari AC, Dreicer R, Sims RB et al (2010) Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med 363:411–422

  59. 59.

    Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L, Vogelzang NJ, Climent MA, Petrylak DP, Choueiri TK et al (2017) Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med 376:1015–1026

  60. 60.

    Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E et al (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med 373:1627–1639

  61. 61.

    Ferris RL, Blumenschein G Jr, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C et al (2016) Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 375:1856–1867

  62. 62.

    Herbst RS, Baas P, Kim DW, Felip E, Perez-Gracia JL, Han JY, Molina J, Kim JH, Arvis CD, Ahn MJ et al (2016) Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387:1540–1550

  63. 63.

    Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, Tykodi SS, Sosman JA, Procopio G, Plimack ER et al (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 373:1803–1813

  64. 64.

    Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, von Pawel J, Gadgeel SM, Hida T, Kowalski DM, Dols MC et al (2017) Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 389:255–265

  65. 65.

    Petrelli F, Coinu A, Cabiddu M, Borgonovo K, Ghilardi M, Lonati V, Barni S (2016) Early analysis of surrogate endpoints for metastatic melanoma in immune checkpoint inhibitor trials. Medicine (Baltimore) 95:e3997

  66. 66.

    Robert C, Thomas L, Bondarenko I, O’Day S, Weber J, Garbe C, Lebbe C, Baurain JF, Testori A, Grob JJ et al (2011) Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med 364:2517–2526

  67. 67.

    Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Hassel JC, Rutkowski P, McNeil C, Kalinka-Warzocha E et al (2015) Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 372:320–330

  68. 68.

    Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Csoszi T, Fulop A, Gottfried M, Peled N, Tafreshi A, Cuffe S et al (2016) Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med 375:1823–1833

  69. 69.

    Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y et al (2009) Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947–957

  70. 70.

    Chen TT (2015) Milestone survival: a potential intermediate endpoint for immune checkpoint inhibitors. J Natl Cancer Inst 107:156

  71. 71.

    Korn EL, Freidlin B (2018) Interim futility monitoring assessing immune therapies with a potentially delayed treatment effect. J Clin Oncol 36:2444–2449

  72. 72.

    Liang F, Zhang S, Wang Q, Li W (2018) Treatment effects measured by restricted mean survival time in trials of immune checkpoint inhibitors for cancer. Ann Oncol 29:1320–1324

  73. 73.

    Pak K, Uno H, Kim DH, Tian L, Kane RC, Takeuchi M, Fu H, Claggett B, Wei LJ (2017) Interpretability of cancer clinical trial results using restricted mean survival time as an alternative to the hazard ratio. JAMA Oncol 3:1692–1696

  74. 74.

    Peron J, Lambert A, Munier S, Ozenne B, Giai J, Roy P, Dalle S, Machingura A, Maucort-Boulch D, Buyse M (2019) Assessing long-term survival benefits of immune checkpoint inhibitors using the net survival benefit. J Natl Cancer Inst 111:1186–1191

  75. 75.

    Hoering A, Durie B, Wang H, Crowley J (2017) End points and statistical considerations in immuno-oncology trials: impact on multiple myeloma. Fut Oncol 13:1181–1193

  76. 76.

    Huang B, Kuan PF (2018) Comparison of the restricted mean survival time with the hazard ratio in superiority trials with a time-to-event end point. Pharm Stat 17:202–213

  77. 77.

    Xu Z, Zhen B, Park Y, Zhu B (2017) Designing therapeutic cancer vaccine trials with delayed treatment effect. Stat Med 36:592–605

  78. 78.

    Rahman R, Fell G, Trippa L, Alexander BM (2018) Violations of the proportional hazards assumption in randomized phase III oncology clinical trials. J Clin Oncol 36 (15 Suppl):abstract 2543

  79. 79.

    Lin NX, Logan S, Henley WE (2013) Bias and sensitivity analysis when estimating treatment effects from the cox model with omitted covariates. Biometrics 69:850–860

  80. 80.

    Harrington DP, Fleming TR (1982) A class of rank test procedures for censored survival data. Biometrika 69:133–143

  81. 81.

    Lin RS, Leon LF (2017) Estimation of treatment effects in weighted log-rank tests. Contemp Clin Trials Commun 8:147–155

  82. 82.

    Zucker M, Lakatos E (1990) Weighted log rank type statistics for comparing survival curves when there is a time lag in the effectiveness of treatment. Biometrika 77:853–864

  83. 83.

    Yang S, Prentice R (2010) Improved logrank-type tests for survival data using adaptive weights. Biometrics 66:30–38

  84. 84.

    Magirr D, Burman CF (2019) Modestly weighted logrank tests. Stat Med 38:3782–3790

  85. 85.

    Cohen EEW, Soulieres D, Le Tourneau C, Dinis J, Licitra L, Ahn MJ, Soria A, Machiels JP, Mach N, Mehra R et al (2019) Pembrolizumab versus methotrexate, docetaxel, or cetuximab for recurrent or metastatic head-and-neck squamous cell carcinoma (KEYNOTE-040): a randomised, open-label, phase 3 study. Lancet 393:156–167

  86. 86.

    Su Z, Zhu M (2018) Is it time for the weighted log-rank test to play a more important role in confirmatory trials? Contemp Clin Trials Commun 10:A1–A2

  87. 87.

    Freidlin B, Korn EL (2019) Methods for accommodating nonproportional hazards in clinical trials: ready for the primary analysis? J Clin Oncol 37:3455–3459

  88. 88.

    Chapman JW, O’Callaghan CJ, Hu N, Ding K, Yothers GA, Catalano PJ, Shi Q, Gray RG, O’Connell MJ, Sargent DJ (2013) Innovative estimation of survival using log-normal survival modelling on ACCENT database. Br J Cancer 108:784–790

  89. 89.

    Chapman JA, Lickley HL, Trudeau ME, Hanna WM, Kahn HJ, Murray D, Sawka CA, Mobbs BG, McCready DR, Pritchard KI (2006) Ascertaining prognosis for breast cancer in node-negative patients with innovative survival analysis. Breast J 12:37–47

  90. 90.

    Senders JT, Staples P, Mehrtash A, Cote DJ, Taphoorn MJB, Reardon DA, Gormley WB, Smith TR, Broekman ML, Arnaout O (2019) An online calculator for the prediction of survival in glioblastoma patients using classical statistics and machine learning. Neurosurgery 86:184–192

  91. 91.

    Anderson KM (1991) A nonproportional hazards Weibull accelerated failure time regression model. Biometrics 47:281–288

  92. 92.

    Odell PM, Anderson KM, Kannel WB (1994) New models for predicting cardiovascular events. J Clin Epidemiol 47:583–592

  93. 93.

    Buckley J, James I (1979) Linear regression with censored data. Biometrika 66:429–436

  94. 94.

    Prentice RL (1978) Linear rank tests with censored data. Biometrika 65:167–179

  95. 95.

    Chiou SH, Kang S, Yan J (2014) Fitting accelerated failure time models in routine survival analysis with R package aftgee. J Stat Softw 61:1–23

  96. 96.

    Royston P, Parmar MK (2011) The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Stat Med 30:2409–2421

  97. 97.

    Royston P, Parmar MK (2013) Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med Res Methodol 13:152

  98. 98.

    Seruga B, Pond GR, Hertz PC, Amir E, Ocana A, Tannock IF (2012) Comparison of absolute benefits of anticancer therapies determined by snapshot and area methods. Ann Oncol 23:2977–2982

  99. 99.

    Trinquart L, Jacot J, Conner SC, Porcher R (2016) Comparison of treatment effects measured by the hazard ratio and by the ratio of restricted mean survival times in oncology randomized controlled trials. J Clin Oncol 34:1813–1819

  100. 100.

    A’Hern RP (2016) Restricted mean survival time: an obligatory end point for time-to-event analysis in cancer trials? J Clin Oncol 34:3474–3476

  101. 101.

    Tian L, Fu H, Ruberg SJ, Uno H, Wei LJ (2018) Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations. Biometrics 74:694–702

  102. 102.

    Luo X, Huang B, Quan H (2019) Design and monitoring of survival trials based on restricted mean survival times. Clin Trials 16:616–625

  103. 103.

    Karrison T (2016) Versatile tests for comparing survival curves based on weighted log-rank statistics. Stata J 16:678–690

  104. 104.

    Lee JW (1996) Some versatile tests based on the simultaneous use of weighted log-rank statistics. Biometrics 52:721–725

  105. 105.

    Chi Y, Tsai MH (2001) Some versatile tests based on the simultaneous use of weighted logrank and weighted Kaplan-Meier statistics. Commun Stat Simulat 30:743–759

  106. 106.

    Pepe MS, Fleming TR (1989) Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data. Biometrics 45:497–507

  107. 107.

    Royston P, Parmar MK (2016) Augmenting the logrank test in the design of clinical trials in which non-proportional hazards of the treatment effect may be anticipated. BMC Med Res Methodol 16:16

  108. 108.

    Royston P, Choodari-Oskooei B, Parmar MKB, Rogers JK (2019) Combined test versus logrank/Cox test in 50 randomised trials. Trials 20:172

  109. 109.

    Powles T, Duran I, van der Heijden MS, Loriot Y, Vogelzang NJ, De Giorgi U, Oudard S, Retz MM, Castellano D, Bamias A et al (2018) Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet 391:748–757

  110. 110.

    Roychoudhury S, Anderson KM, Ye J, Mukhopadhyay P (2019) Robust design and analysis of clinical trials with non-proportional hazards: a straw man guidance from a cross-pharma Working Group. https://arxiv.org/abs/1908.07112

  111. 111.

    Buyse M (2010) Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. Stat Med 29:3245–3257

  112. 112.

    Pocock SJ, Ariti CA, Collier TJ, Wang D (2012) The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur Heart J 33:176–182

  113. 113.

    Peron J, Buyse M, Ozenne B, Roche L, Roy P (2018) An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring. Stat Methods Med Res 27:1230–1239

  114. 114.

    Peron J, Roy P, Ozenne B, Roche L, Buyse M (2016) The net chance of a longer survival as a patient-oriented measure of treatment benefit in randomized clinical trials. JAMA Oncol 2:901–905

  115. 115.

    Buyse M (2019) Multiple prioritized outcomes. Wiley StatsRef: Statistics Reference Online. https://doi.org/10.1002/9781118445112.stat08158

  116. 116.

    Evans SR, Follmann D (2016) Using outcomes to analyze patients rather than patients to analyze outcomes: a step toward pragmatism in benefit: risk evaluation. Stat Biopharm Res 8:386–393

Download references

Author information

Correspondence to Marc Buyse.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Buyse, M., Saad, E.D., Burzykowski, T. et al. Assessing Treatment Benefit in Immuno-oncology. Stat Biosci (2020). https://doi.org/10.1007/s12561-020-09268-1

Download citation

Keywords

  • Immuno-oncology
  • Immune RECIST
  • Weighted logrank test
  • Accelerated failure time model
  • Restricted mean survival time
  • Generalized pairwise comparisons