Feldman H A, McKinlay S M
New England Research Institute, Inc., Watertown, MA 02172, USA.
Stat Med. 1994 Jan 15;13(1):61-78. doi: 10.1002/sim.4780130108.
In planning large longitudinal field trials, one is often faced with a choice between a cohort design and a cross-sectional design, with attendant issues of precision, sample size, and bias. To provide a practical method for assessing these trade-offs quantitatively, we present a unifying statistical model that embraces both designs as special cases. The model takes account of continuous and discrete endpoints, site differences, and random cluster and subject effects of both a time-invariant and a time-varying nature. We provide a comprehensive design equation, relating sample size to precision for cohort and cross-sectional designs, and show that the follow-up cost and selection bias attending a cohort design may outweigh any theoretical advantage in precision. We provide formulae for the minimum number of clusters and subjects. We relate this model to the recently published prevalence model for COMMIT, a multi-site trial of smoking cessation programmes. Finally, we tabulate parameter estimates for some physiological endpoints from recent community-based heart-disease prevention trials, work an example, and discuss the need for compiling such estimates as a basis for informed design of future field trials.
在规划大型纵向现场试验时，人们常常面临队列设计和横断面设计之间的选择，以及随之而来的精度、样本量和偏差问题。为了提供一种定量评估这些权衡的实用方法，我们提出了一个统一的统计模型，该模型将这两种设计作为特殊情况包含在内。该模型考虑了连续和离散的终点、地点差异以及时间不变和随时间变化的随机聚类和个体效应。我们提供了一个综合设计方程，将队列设计和横断面设计的样本量与精度联系起来，并表明队列设计的随访成本和选择偏差可能超过精度方面的任何理论优势。我们提供了聚类和个体的最小数量公式。我们将此模型与最近发表的COMMIT（一项戒烟计划的多中心试验）患病率模型联系起来。最后，我们列出了近期社区心脏病预防试验中一些生理终点的参数估计值，给出了一个示例，并讨论了编制此类估计值作为未来现场试验明智设计基础的必要性。
