Cross-Sectional Analysis of Time-Dependent Data: Mean-Induced Association in Age-Heterogeneous Samples and an Alternative Method Based on Sequential Narrow Age-Cohort Samples.

The effect of time-related mean differences on estimates of association in cross-sectional studies has not been widely recognized in developmental and aging research. Cross-sectional studies of samples varying in age have found moderate to high levels of shared age-related variance among diverse age-related measures. These findings may be misleading because high levels of association between time-dependent processes can result simply from average population age differences and not necessarily from associations between individual "rates of aging." This is demonstrated both analytically and in a simulation involving cross-sectional sampling of individual trajectories. An alternative cross-sectional narrow age-cohort design is shown to provide a useful alternative for evaluating the interdependence of time-related processes.