Helms R W
Department of Biostatistics, University of North Carolina, Chapel Hill 27599-7400.
Stat Med. 1992 Oct-Nov;11(14-15):1889-913. doi: 10.1002/sim.4780111411.
Longitudinal designs are important in medical research and in many other disciplines. Complete longitudinal studies, in which each subject is evaluated at each measurement occasion, are often very expensive and motivate a search for more efficient designs. Recently developed statistical methods foster the use of intentionally incomplete longitudinal designs that have the potential to be more efficient than complete designs. Mixed models provide appropriate data analysis tools. Fixed effect hypotheses can be tested via a recently developed test statistic, FH. An accurate approximation of the statistic's small sample non-central distribution makes power computations feasible. After reviewing some longitudinal design terminology and mixed model notation, this paper summarizes the computation of FH and approximate power from its non-central distribution. These methods are applied to obtain a large number of intentionally incomplete full-span designs that are more powerful and/or less costly alternatives to a complete design. The source of the greater efficiency of incomplete designs and potential fragility of incomplete designs to randomly missing data are discussed.
纵向设计在医学研究和许多其他学科中都很重要。完整的纵向研究，即对每个受试者在每个测量时刻进行评估，往往成本很高，这促使人们寻求更有效的设计。最近开发的统计方法促进了有意不完全纵向设计的使用，这种设计有可能比完整设计更有效。混合模型提供了合适的数据分析工具。固定效应假设可以通过最近开发的检验统计量FH进行检验。该统计量小样本非中心分布的精确近似使得功效计算成为可能。在回顾了一些纵向设计术语和混合模型符号后，本文总结了FH的计算方法以及从其非中心分布得出的近似功效。这些方法被用于获得大量有意不完全的全跨度设计，这些设计是比完整设计更强大和/或成本更低的替代方案。文中还讨论了不完全设计更高效率的来源以及不完全设计对随机缺失数据的潜在脆弱性。
