Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA.
Department of Systems, Populations and Leadership, School of Nursing, University of Michigan, Ann Arbor, Michigan, USA.
Int J Methods Psychiatr Res. 2022 Sep;31(3):e1916. doi: 10.1002/mpr.1916. Epub 2022 May 18.
Longitudinal survey data allow for the estimation of developmental trajectories of substance use from adolescence to young adulthood, but these estimates may be subject to attrition bias. Moreover, there is a lack of consensus regarding the most effective statistical methodology to adjust for sample selection and attrition bias when estimating these trajectories. Our objective is to develop specific recommendations regarding adjustment approaches for attrition in longitudinal surveys in practice.
Analyzing data from the national U.S. Monitoring the Future panel study following four cohorts of individuals from modal ages 18 to 29/30, we systematically compare alternative approaches to analyzing longitudinal data with a wide range of substance use outcomes, and examine the sensitivity of inferences regarding substance use prevalence and trajectories as a function of college attendance to the approach used.
Our results show that analyzing all available observations in each wave, while simultaneously accounting for the correlations among repeated observations, sample selection, and attrition, is the most effective approach. The adjustment effects are pronounced in wave-specific descriptive estimates but generally modest in covariate-adjusted trajectory modeling.
The adjustments can refine the precision, and, to some extent, the implications of our findings regarding young adult substance use trajectories.
纵向调查数据允许从青春期到青年期估计物质使用的发展轨迹,但这些估计可能受到流失偏差的影响。此外,在估计这些轨迹时,对于调整样本选择和流失偏差的最有效统计方法,尚未达成共识。我们的目标是针对实践中纵向调查中的流失调整方法制定具体建议。
我们分析了来自美国全国监测未来小组研究的四组个体(年龄在 18 至 29/30 岁之间)的数据,系统比较了使用各种物质使用结果分析纵向数据的替代方法,并研究了关于物质使用流行率和轨迹的推论对使用方法的敏感性,因为这取决于大学入学情况。
我们的结果表明,在每个波中分析所有可用的观察值,同时考虑到重复观察、样本选择和流失之间的相关性,是最有效的方法。调整效果在特定波的描述性估计中很明显,但在协变量调整的轨迹建模中通常较小。
这些调整可以提高我们关于年轻人物质使用轨迹的发现的准确性,在一定程度上可以改进这些发现的意义。