雙向最小化法:一個新穎的試驗分派方法

Abstract

個案的隨機分派是臨床試驗重要的特性。若試驗樣本數過少或需要平衡的預後因子較多時，最小化法是一個能有效達到平衡的試驗分派方法。本研究提出一個新的最小化法--雙向最小化法(two-way minimization)。雙向最小化法分別計算人數的不平衡以及預後因子分布的不平衡。分派個案時，藉由機率自兩種不平衡面向中選擇其中之一進行調整，縮小該面向之不平衡程度。本研究以蒙地卡羅模擬比較雙向最小化法與其他五種方法的表現。在模擬研究中，雙向最小化法皆能得到準確的型一誤差率。當試驗中存在重要的預後因子時，雙向最小化法能保持高的統計檢力與小的估計變異，同時又能避免選樣的偏差。此方法可於個案進入試驗後立即進行分派且後續的資料分析使用一般的分析方法即可。本研究所提出的雙向最小化法可選擇作為平衡試驗預後因子的分派方法。

Random allocation of subjects is the hallmark of clinical trials. If a trial entails very few subjects and has many prognostic factors to be balanced, minimization is a more efficient method to achieve balance. In this paper, we propose a novel minimization method, the ‘two-way minimization’. The method separately calculates ‘the imbalance in the total numbers of subjects’ and ‘the imbalance in the distributions of prognostic factors’. And then to allocate a subject, it chooses—by probability—to minimize either one of these two aspects of imbalances. We perform Monte-Carlo simulations to compare the performances of the two-way minimization with five existing randomization methods. We found that the two-way minimization has the correct type I error rates. It also can achieve the highest power and the smallest variance among methods that are resistant to selection bias, when there are a number of important prognostic factors to be balanced in the study. The allocation can be done in real time and the subsequent data analysis is straightforward. The proposed two-way minimization is the method of choice for balancing prognostic factors in a trial.