Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data
AU - Kunz, Cornelia Ursula
AU - Friede, Tim
AU - Parsons, Nick
AU - Todd, Susan
AU - Stallard, Nigel
N1 - © 2014 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
PY - 2014/7
Y1 - 2014/7
N2 - Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.
AB - Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.
KW - Clinical Trials, Phase II as Topic
KW - Clinical Trials, Phase III as Topic
KW - Computer Simulation
KW - Data Interpretation, Statistical
KW - Decision Making
KW - Humans
KW - Randomized Controlled Trials as Topic
KW - Treatment Outcome
U2 - 10.1002/pst.1619
DO - 10.1002/pst.1619
M3 - Journal article
C2 - 24789367
VL - 13
SP - 238
EP - 246
JO - Pharmaceutical Statistics
JF - Pharmaceutical Statistics
SN - 1539-1604
IS - 4
ER -