Kalish L A, Begg C B
Biometrics. 1987 Mar;43(1):145-67.
Recent research in the design of observational epidemiologic studies has been focused on determining which design is most efficient for controlling a potential confounding factor in the analysis of the disease-exposure relationship. Typically, only two candidate designs have been considered, the matched design and the random sample design. These are merely two of an infinite variety of potential designs in which the distributions of the confounder in the comparison groups and the ratio of group sample sizes are arbitrarily chosen. Only in special cases will either of the standard designs be the most efficient or "optimal" design. We construct optimal designs by minimizing the variance of the desired estimate of effect with respect to the controllable design parameters. Construction of an optimal design depends on unknown parameters, so that in practice only an approximately optimal design, perhaps constructed sequentially, is possible. We evaluate the potential usefulness of optimal designs by identifying circumstances in which an optimal design results in large efficiency gains relative to both the matched and random sample designs. We find that there can be substantial efficiency gains in follow-up studies when both the exposure and confounder are strong risk factors. Practical issues in the implementation of these designs are discussed.
近期关于观察性流行病学研究设计的研究主要集中在确定哪种设计在分析疾病-暴露关系时最有效地控制潜在混杂因素。通常，仅考虑了两种候选设计，即匹配设计和随机抽样设计。这些仅仅是无穷多种潜在设计中的两种，其中比较组中混杂因素的分布以及组样本量的比例是任意选择的。只有在特殊情况下，标准设计中的任何一种才会是最有效或“最优”的设计。我们通过相对于可控设计参数最小化所需效应估计值的方差来构建最优设计。最优设计的构建依赖于未知参数，因此在实践中，可能只能构建一个近似最优设计，或许是按顺序构建。我们通过确定相对于匹配设计和随机抽样设计，最优设计能带来大幅效率提升的情况，来评估最优设计的潜在实用性。我们发现，当暴露因素和混杂因素都是强风险因素时，随访研究中可以有显著的效率提升。还讨论了实施这些设计的实际问题。
