Galbraith Sally, Bowden Jack, Mander Adrian
1 School of Mathematics and Statistics, The University of New South Wales, Australia.
2 MRC Biostatistics Unit, Cambridge, UK.
Stat Methods Med Res. 2017 Feb;26(1):374-398. doi: 10.1177/0962280214547150. Epub 2016 Jul 11.
Longitudinal studies are often used to investigate age-related developmental change. Whereas a single cohort design takes a group of individuals at the same initial age and follows them over time, an accelerated longitudinal design takes multiple single cohorts, each one starting at a different age. The main advantage of an accelerated longitudinal design is its ability to span the age range of interest in a shorter period of time than would be possible with a single cohort longitudinal design. This paper considers design issues for accelerated longitudinal studies. A linear mixed effect model is considered to describe the responses over age with random effects for intercept and slope parameters. Random and fixed cohort effects are used to cope with the potential bias accelerated longitudinal designs have due to multiple cohorts. The impact of other factors such as costs and the impact of dropouts on the power of testing or the precision of estimating parameters are examined. As duration-related costs increase relative to recruitment costs the best designs shift towards shorter duration and eventually cross-sectional design being best. For designs with the same duration but differing interval between measurements, we found there was a cutoff point for measurement costs relative to recruitment costs relating to frequency of measurements. Under our model of 30% dropout there was a maximum power loss of 7%.
纵向研究常用于调查与年龄相关的发育变化。单一队列设计选取一组初始年龄相同的个体并随时间跟踪他们，而加速纵向设计则选取多个单一队列，每个队列从不同年龄开始。加速纵向设计的主要优点是，与单一队列纵向设计相比，它能够在更短的时间内涵盖感兴趣的年龄范围。本文探讨了加速纵向研究的设计问题。考虑使用线性混合效应模型来描述随年龄变化的反应，并对截距和斜率参数采用随机效应。随机和固定队列效应用于应对加速纵向设计因多个队列而可能产生的偏差。研究了其他因素的影响，如成本以及失访对检验效能或参数估计精度的影响。随着与持续时间相关的成本相对于招募成本增加，最佳设计转向持续时间更短的设计，最终横断面设计成为最佳。对于持续时间相同但测量间隔不同的设计，我们发现相对于招募成本，测量成本存在一个与测量频率相关的临界点。在我们30%失访率的模型下，最大效能损失为7%。
