Nonparametric Methods for Stratified Two-Sample Designs with Application to Multiclinic Trials
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分层两样本设计的非参数方法及多中心临床试验应用
- 研究问题
- 多中心两样本研究中，如何定义和估计非参数效应，处理中心与交互作用的固定或随机设定及不平衡和并列观测？
- 方法与思路
- 作者以跨中心的总体秩构造线性秩统计量，并在固定与混合模型下定义效应和假设，分析渐近及小样本性质。
- 材料陈述的贡献
- 摘要称程序适用于不平衡设计和包括离散有序数据在内的并列观测，并讨论基线与终点重复观测的混合模型。
- 阅读关注点
- 应核对非参数效应的分布比较含义、中心加权方式，以及固定与随机中心假设如何改变估计目标和推断。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；效应公式、抽样条件和模拟数值未提供，临床试验背景本身不保证因果识别。
原文摘要
Motivated by some problems arising from multiclinic trials, we consider stratified two-sample designs. Nonparametric effects are defined and nonparametric hypotheses are formulated in a design where treatment, centers (strata), and interactions are assumed to be fixed factors. The interpretation of the nonparametric effects and hypotheses is analyzed in two classes of semiparametric models: the linear models and models with Lehmann alternatives. The case where centers and interactions are assumed to be random factors, the so-called mixed model, is also considered. Nonparametric effects and hypotheses are defined for general models, and their properties are analyzed in corresponding linear models and in models with Lehmann alternatives. The nonparametric effects are estimated by linear rank statistics where the ranks over all centers are used. The mixed model for repeated (baseline and endpoint) observations is briefly considered, and rank procedures are also proposed for this model. All procedures are related to the nonparametric effects and are not restricted to the two classes of semiparametric models, which are used only for interpretation of the nonparametric effects. Moreover, we do not assume continuity of the underlying distribution functions of the observations, to be as general as possible. We exclude only the trivial case where the distribution function arises from a point mass; that is, a “one-point distribution.” Thus, not only data coming from continuous distribution functions, but also data with ties—especially discrete ordinal data—can be handled with the proposed procedures. In all cases, the results are derived for unbalanced designs so that there are no restrictions for practical applications. The small-sample properties of the proposed statistics are investigated by simulation studies, and the relevant asymptotic distribution theory is considered. Applications of the proposed procedures are demonstrated by means of examples related to multicenter clinical trials.