Longitudinal Associations Between Parental SES and Adolescent Health-Related Quality of Life Using Growth Curve Modeling

We evaluate how two aspects of socioeconomic status, parental objective and subjective social status, are associated with health-related quality of life in three racial/ethnic groups during pre- to mid-adolescent development, using growth curve modeling. In a longitudinal cohort study, 4,048 Black, Latinx, or White adolescents were assessed in 5th, 7th and 10th grade. Objective social status (OSS) was based on parent-reported highest household educational attainment and total household income, and subjective social status (SSS) was measured with the MacArthur Scale, through which parents indicated their social standing in reference to community and national norms. Adolescents completed the Pediatric Quality of Life Inventory to measure their physical and psychosocial health-related quality of life. Based on growth curve modeling of the intercept, adolescents whose parents have higher OSS levels reported better health-related quality of life, whereas parental SSS was not associated with adolescent health-related quality of life in 5^th grade. These findings were largely consistent across racial/ethnic groups. Based on growth curve modeling of the developmental slopes (or growth curves), higher objective social status was associated with a slower positive growth of health-related quality of life from 5^th to 10^th grade, which was largely consistent across racial/ethnic groups. The opposite appeared for parental SSS and physical health-related quality of life, and only for Latinx, where higher parental perceived social status was associated with an acceleration of positive change. Family income and education may influence health-related quality of life at the start of adolescence, but its effect appears to diminish as youth mature. However, in Latinx youth, parents’ perceived social status may continue to influence improvement in their physical health-related quality of life as they mature.

     

Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation

There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions. We highlight these advantages through a data set including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist. In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis. Although our example is from the trauma field, the techniques we illustrate can be applied to any field.