Novartis Pharma AG, Basel, Switzerland.
Stat Med. 2011 Dec 30;30(30):3475-87. doi: 10.1002/sim.4376. Epub 2011 Sep 27.
The design of a comparative clinical trial involves a method of allocating treatments to patients. Usually, this assignment is performed to achieve several objectives: to minimize selection and accidental bias, to achieve balanced treatment assignment in order to maximize the power of the comparison, and most importantly, to obtain the basis for a valid statistical inference. In this paper, we are concerned exclusively with the last point. In our investigation, we will assume that measurements can be decomposed in a patient-specific effect, a treatment effect, and a measurement error. If the patient can be considered to be randomly drawn from a population, the randomization method does not affect the analysis. In fact, under this so-called population model, randomization would be unnecessary to obtain a valid inference. However, when individuals cannot be considered randomly selected, the patient effects may become fixed but unknown constants. In this case, randomization is necessary to obtain valid statistical analyses, and it cannot be precluded that the randomization method has an impact on the results. This paper elaborates that the impact can be substantial even for a two-sample comparison when a standard t-test is used for data analysis. We provide some theoretical results as well as simulations.
一项对比临床试验的设计涉及到将治疗方法分配给患者的方法。通常，这种分配是为了实现几个目标：最小化选择和偶然偏差，实现治疗分配的平衡，以最大化比较的功效，最重要的是，为有效的统计推断提供依据。本文仅关注最后一点。在我们的研究中，我们假设测量可以分解为患者特定效应、治疗效应和测量误差。如果患者可以被认为是从总体中随机抽取的，那么随机化方法不会影响分析。实际上，在这种所谓的总体模型下，随机化对于获得有效的推断是不必要的。然而，当个体不能被视为随机选择时，患者效应可能成为固定但未知的常数。在这种情况下，随机化是获得有效统计分析所必需的，并且不能排除随机化方法对结果有影响。本文详细阐述了当使用标准 t 检验进行数据分析时，即使对于两样本比较，这种影响也可能是实质性的。我们提供了一些理论结果和模拟。
