Treatment effect quantification for time‐to‐event endpoints–Estimands, analysis strategies, and beyond

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend ICH E9 grew out of the realization of a lack of alignment between the objectives of a clinical trial stated in the protocol and the accompanying quantification of the "treatment effect" reported in a regulatory submission. We embed time-to-event endpoints in the estimand framework and discuss how the four estimand attributes described in the addendum apply to time-to-event endpoints. We point out that if the proportional hazards assumption is not met, the estimand targeted by the most prevalent methods used to analyze time-to-event endpoints, logrank test, and Cox regression depends on the censoring distribution. We discuss for a large randomized clinical trial how the analyses for the primary and secondary endpoints as well as the sensitivity analyses actually performed in the trial can be seen in the context of the addendum. To the best of our knowledge, this is the first attempt to do so for a trial with a time-to-event endpoint. Questions that remain open with the addendum for time-to-event endpoints and beyond are formulated, and recommendations for planning of future trials are given. We hope that this will provide a contribution to developing a common framework based on the final version of the addendum that can be applied to design, protocols, statistical analysis plans, and clinical study reports in the future.

[1]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[2]  T. Hickish,et al.  Oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment for colon cancer. , 2004, The New England journal of medicine.

[3]  David J. Lunn,et al.  Survival extrapolation using the poly-Weibull model , 2015, Statistical methods in medical research.

[4]  Ronald B. Geskus,et al.  Data analysis with competing risks and intermediate states , 2015 .

[5]  F. Bretz,et al.  Estimands and Their Role in Clinical Trials , 2017 .

[6]  M. Pike,et al.  Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. analysis and examples. , 1977, British Journal of Cancer.

[7]  Bryan E Shepherd,et al.  Sensitivity Analyses Comparing Time-to-Event Outcomes Existing Only in a Subset Selected Postrandomization , 2007, Journal of the American Statistical Association.

[8]  Martin Schumacher,et al.  Competing Risks and Multistate Models with R , 2011 .

[9]  Marion Procter,et al.  Adjuvant Pertuzumab and Trastuzumab in Early HER2‐Positive Breast Cancer , 2017, The New England journal of medicine.

[10]  James M Robins,et al.  Per-Protocol Analyses of Pragmatic Trials. , 2017, The New England journal of medicine.

[11]  Miguel A Hernán,et al.  The hazards of hazard ratios. , 2010, Epidemiology.

[12]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

[13]  M. Akritas,et al.  with censored data , 2003 .

[14]  D. Mehrotra,et al.  Seeking harmony: estimands and sensitivity analyses for confirmatory clinical trials , 2016, Clinical trials.

[15]  Sigrid Stroobants,et al.  Revised response criteria for malignant lymphoma. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  D. G. Altman,et al.  Review of survival analyses published in cancer journals. , 1995, British Journal of Cancer.

[17]  Richard Pazdur,et al.  Endpoints for assessing drug activity in clinical trials. , 2008, The oncologist.

[18]  Y. Chiba Kaplan–Meier curves for survivor causal effects with time-to-event outcomes , 2013, Clinical trials.

[19]  Bengt Glimelius,et al.  Survival endpoints in colorectal cancer and the effect of second primary other cancer on disease free survival , 2011, BMC Cancer.

[20]  Sung-Bae Kim,et al.  Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. , 2012, The New England journal of medicine.

[21]  Fang Chen,et al.  Use of historical control data for assessing treatment effects in clinical trials , 2014, Pharmaceutical statistics.

[22]  S. Mathoulin-Pélissier,et al.  Survival end point reporting in randomized cancer clinical trials: a review of major journals. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  Daniel J Sargent,et al.  Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  Tim Friede,et al.  On estimands and the analysis of adverse events in the presence of varying follow‐up times within the benefit assessment of therapies , 2018, Pharmaceutical statistics.

[25]  Robert Gray,et al.  A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .

[26]  J. Thigpen Issues in Using Progression-Free Survival When Evaluating Oncology Products , 2010 .

[27]  J O'Quigley,et al.  Estimating average regression effect under non-proportional hazards. , 2000, Biostatistics.

[28]  D. Moher,et al.  CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials , 2010, Journal of pharmacology & pharmacotherapeutics.

[29]  J. Cerhan,et al.  Event-free survival at 24 months is a robust end point for disease-related outcome in diffuse large B-cell lymphoma treated with immunochemotherapy. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[31]  Daniel L. Gillen,et al.  Robust inference in discrete hazard models for randomized clinical trials , 2012, Lifetime Data Analysis.

[32]  Xin Huang,et al.  Adjusting overall survival for treatment switches: commonly used methods and practical application , 2013, Pharmaceutical statistics.

[33]  S Michiels,et al.  Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)†. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[34]  Claudia Schmoor,et al.  Statistical issues in the analysis of adverse events in time‐to‐event data , 2016, Pharmaceutical statistics.

[35]  C. Porta,et al.  Guidelines for the definition of time-to-event end points in renal cell cancer clinical trials: results of the DATECAN project†. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[36]  Björn Holzhauer,et al.  Choice of estimand and analysis methods in diabetes trials with rescue medication , 2015, Pharmaceutical statistics.

[37]  Robert Ford,et al.  iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. , 2017, The Lancet. Oncology.

[38]  J. Burke,et al.  Obinutuzumab or Rituximab Plus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone in Previously Untreated Diffuse Large B-Cell Lymphoma. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Douglas G Altman,et al.  The logrank test , 2004, BMJ : British Medical Journal.

[40]  R. Labianca,et al.  Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials. , 2007, Journal of the National Cancer Institute.

[41]  Karin Haustermans,et al.  Guidelines for time-to-event end-point definitions in trials for pancreatic cancer. Results of the DATECAN initiative (Definition for the Assessment of Time-to-event End-points in CANcer trials). , 2014, European journal of cancer.

[42]  Thomas Filleron,et al.  Protocol of the Definition for the Assessment of Time-to-event Endpoints in CANcer trials (DATECAN) project: formal consensus method for the development of guidelines for standardised time-to-event endpoints' definitions in cancer clinical trials. , 2013, European journal of cancer.

[43]  R. Batra,et al.  Absence of Evidence Is Not Evidence of Absence , 2019, The American journal of bioethics : AJOB.

[44]  S. Ruberg,et al.  Estimands in clinical trials – broadening the perspective , 2017, Statistics in medicine.

[45]  D. Rubin Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death , 2006, math/0612783.

[46]  P. Diggle,et al.  Latent Variable Modeling and Applications to Causality , 2017 .

[47]  I. White,et al.  A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes , 2017, Trials.

[48]  Thomas D. Cook,et al.  Introduction to Statistical Methods for Clinical Trials , 2007 .

[49]  J. Robins,et al.  Correcting for non-compliance in randomized trials using rank preserving structural failure time models , 1991 .

[50]  W. D. Ray 4. Modelling Survival Data in Medical Research , 1995 .

[51]  Patrick Royston,et al.  Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome , 2013, BMC Medical Research Methodology.

[52]  Judea Pearl,et al.  Causal Inference , 2010 .

[53]  R. Greil,et al.  Randomized phase III trial comparing biweekly infusional fluorouracil/leucovorin alone or with irinotecan in the adjuvant treatment of stage III colon cancer: PETACC-3. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[54]  L. Kaiser Dynamic randomization and a randomization model for clinical trials data , 2012, Statistics in medicine.

[55]  Richard J. Cook,et al.  Does Cox analysis of a randomized survival study yield a causal treatment effect? , 2015, Lifetime data analysis.

[56]  Sally Hunsberger,et al.  Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[57]  Tai-Tsang Chen Statistical issues and challenges in immuno-oncology , 2013, Journal of Immunotherapy for Cancer.

[58]  M Buyse,et al.  Two or three year disease-free survival (DFS) as a primary end-point in stage III adjuvant colon cancer trials with fluoropyrimidines with or without oxaliplatin or irinotecan: data from 12,676 patients from MOSAIC, X-ACT, PETACC-3, C-06, C-07 and C89803. , 2011, European journal of cancer.

[59]  John D. Kalbfleisch,et al.  Misspecified proportional hazard models , 1986 .

[60]  B. Kahan,et al.  Outcome pre-specification requires sufficient detail to guard against outcome switching in clinical trials: a case study , 2018, Trials.

[61]  Akshay S. Desai,et al.  Angiotensin-neprilysin inhibition versus enalapril in heart failure. , 2014, The New England journal of medicine.

[62]  Daniel L Gillen,et al.  Estimation of treatment effect under non‐proportional hazards and conditionally independent censoring , 2012, Statistics in medicine.

[63]  W. Klapper,et al.  Obinutuzumab for the First‐Line Treatment of Follicular Lymphoma , 2017, The New England journal of medicine.

[64]  Liang Li,et al.  On the propensity score weighting analysis with survival outcome: Estimands, estimation, and inference , 2018, Statistics in medicine.

[65]  P. Solal-Céligny,et al.  Follicular lymphoma international prognostic index , 2006, Blood.

[66]  Andrew Lister,et al.  Rituximab maintenance for 2 years in patients with high tumour burden follicular lymphoma responding to rituximab plus chemotherapy (PRIMA): a phase 3, randomised controlled trial , 2011, The Lancet.

[67]  C. Ritchie,et al.  A review of clinical trial designs used to detect a disease-modifying effect of drug therapy in Alzheimer’s disease and Parkinson’s disease , 2016, BMC Neurology.

[68]  Norbert Benda,et al.  Disentangling estimands and the intention‐to‐treat principle , 2017, Pharmaceutical statistics.

[69]  S. Sleijfer,et al.  Guidelines for time-to-event end point definitions in sarcomas and gastrointestinal stromal tumors (GIST) trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)†. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[70]  Bernhard Hemmer,et al.  Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis , 2017, The New England journal of medicine.