Nested combination tests with a time‐to‐event endpoint using a short‐term endpoint for design adaptations

Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.

[1]  E. Bluhmki,et al.  A statistical model for the dependence between progression‐free survival and overall survival , 2009, Statistics in medicine.

[2]  H. Schäfer,et al.  Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections , 2001, Statistics in medicine.

[3]  K. Gabriel,et al.  On closed testing procedures with special reference to ordered analysis of variance , 1976 .

[4]  Martin Posch,et al.  Adaptive designs for subpopulation analysis optimizing utility functions , 2014, Biometrical journal. Biometrische Zeitschrift.

[5]  P. Bauer,et al.  Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections by H. Schäfer and H.‐H. Müller, Statistics in Medicine 2001; 20: 3741–3751 , 2004, Statistics in medicine.

[6]  Helmut Schäfer,et al.  Interim Design Modifications in Time-to-Event Studies , 2012 .

[7]  Hui Quan,et al.  An Adaptive Timeline Determination of Survival Trials , 2018, Statistics in Biopharmaceutical Research.

[8]  B. Freidlin,et al.  Sample size adjustment designs with time-to-event outcomes: A caution , 2017, Clinical trials.

[9]  R. Uozumi,et al.  Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints , 2017, Journal of biopharmaceutical statistics.

[10]  Gernot Wassmer,et al.  Group Sequential and Confirmatory Adaptive Designs in Clinical Trials , 2016 .

[11]  T. Friede,et al.  Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review , 2016, Journal of biopharmaceutical statistics.

[12]  Cyrus Mehta,et al.  Biomarker driven population enrichment for adaptive oncology trials with time to event endpoints , 2014, Statistics in medicine.

[13]  L Di Scala,et al.  Time‐to‐event analysis with treatment arm selection at interim , 2011, Statistics in medicine.

[14]  C. Jennison,et al.  An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints † , 2011, Pharmaceutical statistics.

[15]  R. Prentice Surrogate endpoints in clinical trials: definition and operational criteria. , 1989, Statistics in medicine.

[16]  M Kieser,et al.  Combining different phases in the development of medical treatments within a single trial. , 1999, Statistics in medicine.

[17]  Thomas R Fleming,et al.  Issues in using progression-free survival when evaluating oncology products. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  D. DeMets,et al.  Surrogate End Points in Clinical Trials: Are We Being Misled? , 1996, Annals of Internal Medicine.

[19]  Frank Bretz,et al.  Twenty‐five years of confirmatory adaptive designs: opportunities and pitfalls , 2015, Statistics in medicine.

[20]  Martin Posch,et al.  Optimized adaptive enrichment designs , 2017, Statistical methods in medical research.

[21]  Gernot Wassmer,et al.  Planning and Analyzing Adaptive Group Sequential Survival Trials , 2006, Biometrical journal. Biometrische Zeitschrift.

[22]  Martin Posch,et al.  Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials , 2016, PloS one.

[23]  Martin Posch,et al.  Testing and estimation in flexible group sequential designs with adaptive treatment selection , 2005, Statistics in medicine.

[24]  Tim Friede,et al.  Adaptive Designs for Confirmatory Clinical Trials with Subgroup Selection , 2014, Journal of biopharmaceutical statistics.

[25]  H. Schäfer,et al.  Adaptive Group Sequential Designs for Clinical Trials: Combining the Advantages of Adaptive and of Classical Group Sequential Approaches , 2001, Biometrics.

[26]  A. Tsiatis,et al.  Approximately optimal one-parameter boundaries for group sequential trials. , 1987, Biometrics.

[27]  P. Bauer,et al.  Evaluation of experiments with adaptive interim analyses. , 1994, Biometrics.

[28]  T Friede,et al.  Designing a seamless phase II/III clinical trial using early outcomes for treatment selection: An application in multiple sclerosis , 2011, Statistics in medicine.

[29]  Kaspar Rufibach,et al.  Comparison of different clinical development plans for confirmatory subpopulation selection. , 2016, Contemporary clinical trials.

[30]  Nigel Stallard,et al.  A confirmatory seamless phase II/III clinical trial design incorporating short‐term endpoint information , 2010, Statistics in medicine.

[31]  C. Weir,et al.  Statistical evaluation of biomarkers as surrogate endpoints: a literature review , 2006, Statistics in medicine.

[32]  A. Azuero A note on the magnitude of hazard ratios , 2016, Cancer.

[33]  T. Fleming,et al.  Biomarkers and surrogate endpoints in clinical trials , 2012, Statistics in medicine.

[34]  G. Hommel Adaptive Modifications of Hypotheses After an Interim Analysis , 2001 .

[35]  David L. DeMets,et al.  Design and analysis of group sequential tests based on the type I error spending rate function , 1987 .

[36]  M. Kendall,et al.  Rank and product-moment correlation. , 1949, Biometrika.

[37]  Georg Gutjahr,et al.  Adaptive seamless designs with interim treatment selection: a case study in oncology , 2015, Statistics in medicine.