Schill W, Wild P
Bremer Institut für Präventionsforschung und Sozialmedizin (BIPS) und Institut für Statistik, Universität Bremen, Germany.
Stat Med. 2006 May 30;25(10):1646-59. doi: 10.1002/sim.2307.
Two-phase designs, in which a subsample with validation or complete information data is sampled stratified both on outcome and covariate from a first-phase study with incomplete data, have been proposed over 10 years ago and have been proven to result in efficient estimates with respect to standard designs. The efficiency depends, however, on the sampling fractions within each stratum. Our aim is to present a strategy for obtaining an optimized design, i.e. sampling fractions, that makes use of available phase-one data of an existing case-control study considered as the first-phase sample when the focus is on estimating a parameter vector. No global optimal design exists and local optimal designs depend on scenarios comprising the true disease model and the association between the phase-one and phase-two information. We develop an admissibility test that rejects scenarios inconsistent with the phase-one data and, for the selected scenarios, determine a minmax D- or A-optimal design that protects against worst-case scenarios. This work is applied on two examples.
两阶段设计在十多年前就已被提出，即在第一阶段研究数据不完整的情况下，从其中抽取一个包含验证或完整信息数据的子样本，该子样本在结局和协变量上均进行分层抽样，并且已被证明相对于标准设计能得到有效估计。然而，效率取决于各层内的抽样比例。我们的目标是提出一种获得优化设计（即抽样比例）的策略，当重点是估计参数向量时，该策略利用现有病例对照研究的可用第一阶段数据作为第一阶段样本。不存在全局最优设计，局部最优设计取决于包括真实疾病模型以及第一阶段和第二阶段信息之间关联的场景。我们开发了一种可容许性检验，用于拒绝与第一阶段数据不一致的场景，并针对选定的场景确定一个最小最大D最优或A最优设计，以防范最坏情况。这项工作应用于两个示例。
