Luningham Justin M, McArtor Daniel B, Bartels Meike, Boomsma Dorret I, Lubke Gitta H
Department of Psychology, University of Notre Dame, 220 C Haggar Hall, Notre Dame, IN, 46556, USA.
Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Behav Genet. 2017 Sep;47(5):516-536. doi: 10.1007/s10519-017-9864-0. Epub 2017 Aug 5.
To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g. a sum score) is a simple and practical approach for modeling such data, but this simplicity can come at a cost in longitudinal studies, where the relevance of individual items often changes as a function of age. Such changes violate the assumptions of longitudinal measurement invariance (MI), and this violation has the potential to obfuscate the interpretation of the results of latent growth models fit to sum scores. The objectives of this study are (1) to investigate the extent to which violations of longitudinal MI lead to bias in parameter estimates of the average growth curve trajectory, and (2) whether absence of MI affects estimates of the heritability of these growth curve parameters. To this end, we analytically derive the bias in the estimated means and variances of the latent growth factors fit to sum scores when the assumption of longitudinal MI is violated. This bias is further quantified via Monte Carlo simulation, and is illustrated in an empirical analysis of aggression in children aged 3-12 years. These analyses show that measurement non-invariance across age can indeed bias growth curve mean and variance estimates, and our quantification of this bias permits researchers to weigh the costs of using a simple sum score in longitudinal studies. Simulation results indicate that the genetic variance decomposition of growth factors is, however, not biased due to measurement non-invariance across age, provided the phenotype is measurement invariant across birth-order and zygosity in twins.
为了研究行为或精神表型，通常会收集行为或障碍的多个指标，这些指标被认为能最好地反映表型。将这些项目合并为一个单一分数（例如总分）是对这类数据进行建模的一种简单实用的方法，但这种简单性在纵向研究中可能会付出代价，因为在纵向研究中，各个项目的相关性通常会随着年龄的变化而改变。这种变化违反了纵向测量不变性（MI）的假设，而这种违反有可能模糊对拟合总分的潜在增长模型结果的解释。本研究的目的是：（1）调查违反纵向MI在多大程度上会导致平均增长曲线轨迹参数估计的偏差；（2）MI的缺失是否会影响这些增长曲线参数遗传力的估计。为此，我们通过分析得出当纵向MI假设被违反时，拟合总分的潜在增长因子估计均值和方差的偏差。通过蒙特卡罗模拟进一步量化这种偏差，并在对3至12岁儿童攻击性的实证分析中进行说明。这些分析表明，年龄间的测量非不变性确实会使增长曲线均值和方差估计产生偏差，我们对这种偏差的量化使研究人员能够权衡在纵向研究中使用简单总分的成本。模拟结果表明，然而，只要表型在双胞胎的出生顺序和合子性方面是测量不变的，由于年龄间的测量非不变性，增长因子的遗传方差分解不会产生偏差。
