To add or not to add a new treatment arm to a multiarm study: A decision‐theoretic framework

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.

[1]  J. Wason,et al.  Correcting for multiple-testing in multi-arm trials: is it necessary and is it done? , 2014, Trials.

[2]  Giovanni Parmigiani,et al.  Adaptive Randomization of Neratinib in Early Breast Cancer. , 2016, The New England journal of medicine.

[3]  J. Q. Smith Decision Analysis: A Bayesian Approach , 1988 .

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

[5]  Jack Bowden,et al.  Identifying combined design and analysis procedures in two-stage trials with a binary end point , 2012, Statistics in medicine.

[6]  Nigel Stallard,et al.  A comparison of methods for constructing confidence intervals after phase II/III clinical trials , 2014, Biometrical journal. Biometrische Zeitschrift.

[7]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[8]  Stefanie Biedermann,et al.  Optimal design for experiments with possibly incomplete observations , 2018 .

[9]  Martin Posch,et al.  Decision-theoretic designs for small trials and pilot studies: A review , 2015, Statistical methods in medical research.

[10]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[11]  Nigel Stallard,et al.  Point estimates and confidence regions for sequential trials involving selection , 2005 .

[12]  Brian P Hobbs,et al.  Controlled multi-arm platform design using predictive probability , 2018, Statistical methods in medical research.

[13]  N. Lane,et al.  Cryoneurolysis to treat the pain and symptoms of knee osteoarthritis: a multicenter, randomized, double-blind, sham-controlled trial. , 2017, Osteoarthritis and cartilage.

[14]  J. Wason,et al.  Optimal multistage designs for randomised clinical trials with continuous outcomes , 2011, Statistics in medicine.

[15]  Lorenzo Trippa,et al.  Adding experimental arms to platform clinical trials: randomization procedures and interim analyses. , 2018, Biostatistics.

[16]  D A Berry,et al.  One-sided sequential stopping boundaries for clinical trials: a decision-theoretic approach. , 1988, Biometrics.

[17]  W. Brannath,et al.  Recursive Combination Tests , 2002 .

[18]  M. Degroot Optimal Statistical Decisions , 1970 .

[19]  Nigel Stallard,et al.  Sequential designs for phase III clinical trials incorporating treatment selection , 2003, Statistics in medicine.

[20]  Thomas Jaki,et al.  Some recommendations for multi-arm multi-stage trials , 2012, Statistical methods in medical research.

[21]  Martin Posch,et al.  Optimal choice of the number of treatments to be included in a clinical trial , 2009, Statistics in medicine.

[22]  Brian M Alexander,et al.  Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE , 2017, Clinical Cancer Research.

[23]  Martin Posch,et al.  Determination of the optimal sample size for a clinical trial accounting for the population size , 2016, Biometrical journal. Biometrische Zeitschrift.

[24]  Ying Yuan,et al.  MIDAS: a practical Bayesian design for platform trials with molecularly targeted agents. , 2016, Statistics in medicine.

[25]  Nigel Stallard,et al.  Optimal sample sizes for phase II clinical trials and pilot studies , 2012, Statistics in medicine.

[26]  G. Parmigiani,et al.  Designing Clinical Trials That Accept New Arms: An Example in Metastatic Breast Cancer. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[27]  Wenle Zhao,et al.  Flexible Analytical Methods for Adding a Treatment Arm Mid-Study to an Ongoing Clinical Trial , 2012, Journal of biopharmaceutical statistics.

[28]  M. Parmar,et al.  Flexible trial design in practice - stopping arms for lack-of-benefit and adding research arms mid-trial in STAMPEDE: a multi-arm multi-stage randomized controlled trial , 2012, Trials.

[29]  Jack Bowden,et al.  Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends? , 2017, Pharmaceutical statistics.

[30]  S. Todd,et al.  Adding a treatment arm to an ongoing clinical trial: a review of methodology and practice , 2015, Trials.

[31]  Nigel Stallard,et al.  Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility , 2013, Statistics in medicine.

[32]  Alex Dmitrienko,et al.  POWER CALCULATIONS IN CLINICAL TRIALS WITH COMPLEX CLINICAL OBJECTIVES , 2015 .

[33]  Kim May Lee,et al.  Optimal design when outcome values are not missing at random , 2018 .

[34]  J. Wason,et al.  Minimizing the Maximum Expected Sample Size in Two-Stage Phase II Clinical Trials with Continuous Outcomes , 2012, Journal of biopharmaceutical statistics.

[35]  Nigel Stallard,et al.  Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials , 2018 .

[36]  Jack Bowden,et al.  A multi-stage drop-the-losers design for multi-arm clinical trials , 2016, Statistical methods in medical research.

[37]  Tim Friede,et al.  A group‐sequential design for clinical trials with treatment selection , 2008, Statistics in medicine.

[38]  Adrian P Mander,et al.  Admissible two‐stage designs for phase II cancer clinical trials that incorporate the expected sample size under the alternative hypothesis , 2012, Pharmaceutical statistics.

[39]  Thomas Jaki,et al.  Optimal design of multi‐arm multi‐stage trials , 2012, Statistics in medicine.