Brown Sarah, Thorpe Helen, Hawkins Kim, Brown Julia
Clinical Trials Research Unit, University of Leeds, 17 Springfield Mount, Leeds LS2 9NG, UK.
Stat Med. 2005 Dec 30;24(24):3715-27. doi: 10.1002/sim.2391.
Minimization is often used to assign patients to treatment groups to ensure good balance in patient numbers within centre and other prognostic factors. Balance within centre is preferable since large imbalances between treatment arms may have logistical implications for centres, such as cost and resource implications. However, recent concern over high predictability of treatment allocation by centres when using minimization has caused this method to be questioned. We used data from current clinical trials to assess predictability and summarize subsequent within-centre imbalances with the aim of finding the most effective minimization method for reducing predictability whilst still retaining sufficient balance within centre, when randomization is to one of two treatments. We compared prediction rates and imbalances for deterministic minimization, and minimization incorporating various random elements, p (p=0.95,0.90,0.80,0.75,0.70). We also compared prediction rates and imbalance when centre was and was not included as a stratification factor. Incorporating a random element proved successful in reducing prediction rates whilst minimizing the inevitable increase in within-centre imbalance, whereas excluding centre as a stratification factor incurred major within-centre imbalance. We therefore suggest that minimization can still be used, and that centre can be included as a stratification factor, but a random element has to be incorporated into the minimization algorithm. Minimization incorporating a random element of 0.80 is the most efficient method to use based upon the simulations undertaken in this study of real clinical trial data using different probabilities of allocation.
最小化法常用于将患者分配到治疗组，以确保中心内患者数量以及其他预后因素的良好平衡。中心内的平衡更为可取，因为治疗组之间的巨大不平衡可能会给中心带来后勤方面的影响，如成本和资源方面的影响。然而，最近有人担心在使用最小化法时中心对治疗分配的高可预测性，这使得这种方法受到质疑。我们使用当前临床试验的数据来评估可预测性，并总结随后的中心内不平衡情况，目的是找到最有效的最小化方法，以在随机分配到两种治疗之一时，降低可预测性同时仍保持中心内足够的平衡。我们比较了确定性最小化法以及包含各种随机因素（p = 0.95、0.90、0.80、0.75、0.70）的最小化法的预测率和不平衡情况。我们还比较了将中心作为分层因素和不将中心作为分层因素时的预测率和不平衡情况。事实证明，纳入随机因素在降低预测率的同时，能将中心内不平衡不可避免的增加降至最低，而不将中心作为分层因素则会导致中心内出现严重不平衡。因此，我们建议仍可使用最小化法，并且可以将中心作为分层因素，但必须在最小化算法中纳入随机因素。根据本研究对使用不同分配概率的真实临床试验数据进行的模拟，纳入随机因素为0.80的最小化法是最有效的方法。
