Research output: Contribution to journal › Article › peer-review
Original language | English |
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Pages (from-to) | 2803-2813 |
Number of pages | 11 |
Journal | Statistics in Medicine |
Volume | 36 |
Issue number | 18 |
Early online date | 5 Jun 2017 |
DOIs | |
Accepted/In press | 26 Jan 2016 |
E-pub ahead of print | 5 Jun 2017 |
Published | 15 Aug 2017 |
Additional links |
Information-adaptive clinical trials_BARRETT_Accepted21January2016_GREEN AAM
Information_adaptive_clinical_trials_BARRETT_Accepted21January2016_GREEN_AAM.pdf, 866 KB, application/pdf
Uploaded date:20 Sep 2018
Version:Accepted author manuscript
Selective recruitment designs preferentially recruit individuals who are estimated to be statistically informative onto a clinical trial. Individuals who are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information-adaptive design, recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values, and how information is defined is a critical element of information-adaptive designs. In this paper, we define and evaluate four different methods for quantifying statistical information. Using both experimental data and numerical simulations, we show that selective recruitment designs can offer a substantial increase in statistical power compared with randomised designs. In trials without selective recruitment, we find that allocating individuals to treatment arms according to information-adaptive protocols also leads to an increase in statistical power. Consequently, selective recruitment designs can potentially achieve successful trials using fewer recruits thereby offering economic and ethical advantages.
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