### Abstract

Original language | English |
---|---|

Pages (from-to) | 5458-5468 |

Number of pages | 11 |

Journal | Statistics in Medicine |

Volume | 32 |

Issue number | 30 |

DOIs | |

Publication status | Published - 30 Dec 2013 |

Externally published | Yes |

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*Statistics in Medicine*,

*32*(30), 5458-5468. https://doi.org/10.1002/sim.5979

}

*Statistics in Medicine*, vol. 32, no. 30, pp. 5458-5468. https://doi.org/10.1002/sim.5979

**Some issues in predicting patient recruitment in multi-centre clinical trials.** / Bakhshi, Andisheh; Senn, Stephen; Phillips, Alan.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Some issues in predicting patient recruitment in multi-centre clinical trials

AU - Bakhshi, Andisheh

AU - Senn, Stephen

AU - Phillips, Alan

PY - 2013/12/30

Y1 - 2013/12/30

N2 - A key paper in modelling patient recruitment in multi-centre clinical trials is that of Anisimov and Fedorov. They assume that the distribution of the number of patients in a given centre in a completed trial follows a Poisson distribution. In a second stage, the unknown parameter is assumed to come from a Gamma distribution. As is well known, the overall Gamma-Poisson mixture is a negative binomial. For forecasting time to completion, however, it is not the frequency domain that is important, but the time domain and that of Anisimov and Fedorov have also illustrated clearly the links between the two and the way in which a negative binomial in one corresponds to a type VI Pearson distribution in the other. They have also shown how one may use this to forecast time to completion in a trial in progress. However, it is not just necessary to forecast time to completion for trials in progress but also for trials that have yet to start. This suggests that what would be useful would be to add a higher level of the hierarchy: over all trials. We present one possible approach to doing this using an orthogonal parameterization of the Gamma distribution with parameters on the real line. The two parameters are modelled separately. This is illustrated using data from 18 trials. We make suggestions as to how this method could be applied in practice. Copyright © 2013 John Wiley & Sons, Ltd.

AB - A key paper in modelling patient recruitment in multi-centre clinical trials is that of Anisimov and Fedorov. They assume that the distribution of the number of patients in a given centre in a completed trial follows a Poisson distribution. In a second stage, the unknown parameter is assumed to come from a Gamma distribution. As is well known, the overall Gamma-Poisson mixture is a negative binomial. For forecasting time to completion, however, it is not the frequency domain that is important, but the time domain and that of Anisimov and Fedorov have also illustrated clearly the links between the two and the way in which a negative binomial in one corresponds to a type VI Pearson distribution in the other. They have also shown how one may use this to forecast time to completion in a trial in progress. However, it is not just necessary to forecast time to completion for trials in progress but also for trials that have yet to start. This suggests that what would be useful would be to add a higher level of the hierarchy: over all trials. We present one possible approach to doing this using an orthogonal parameterization of the Gamma distribution with parameters on the real line. The two parameters are modelled separately. This is illustrated using data from 18 trials. We make suggestions as to how this method could be applied in practice. Copyright © 2013 John Wiley & Sons, Ltd.

U2 - 10.1002/sim.5979

DO - 10.1002/sim.5979

M3 - Article

VL - 32

SP - 5458

EP - 5468

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 30

ER -