Latent Class Models of Early-life Trauma and Incident Breast Cancer

Early life trauma and adult leucocyte telomere length

BACKGROUND

Telomere length, a biomarker of cell division and cellular aging, has been associated with multiple chronic disease endpoints. Experienced trauma over the life course may contribute to telomere shortening via mechanisms of stress embodiment. However, it is unclear how patterns of co-occurring trauma during sensitive periods (e.g., early life) throughout the life course may influence telomere shortening. We examine the relationship between co-occurring early life trauma on adult telomere length and the extent to which adulthood trauma, socioeconomic position, and health and lifestyle factors may mediate this relationship.

METHODS

We use data from a sample of participants in the Sister Study (N = 740, analytic sample: n = 602), a prospective cohort of U.S. self-identified females aged 35-74 years at enrollment (2003-2009) for whom leukocyte telomere length was measured in baseline blood samples. Participants reported their experience of 20 different types of trauma, from which we identified patterns of co-occurring early life trauma (before age 18) using latent class analysis. We estimated the direct and indirect effects of early life trauma on leukocyte telomere length using structural equation modeling, allowing for mediating adult pathways.

RESULTS

Approximately 47 % of participants reported early life trauma. High early life trauma was associated with shorter telomere length compared to low early life trauma (β = -0.11; 95 % CI: -0.22, -0.004) after adjusting for age and childhood socioeconomic position. The inverse association between early life trauma and adult leukocyte telomere length was largely attributable to the direct effect of early life trauma on telomere length (β = -0.12; 95 %CI: -0.23, -0.01). Mediating indirect pathways via adult trauma, socioeconomic position, and health metrics did not substantively contribute the overall association.

CONCLUSIONS

These findings highlight the role of patterns of co-occurring early life trauma on shortened telomere length independent of adult pathways.

Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses

Background

Increasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the individuals into subgroups, then estimating the subgroup-specific average treatment effects. However, because the subgroups are only latently associated with the observed variables, the actual individual subgroup memberships are rarely known with certainty in practice and thus have to be imputed. Ignoring the uncertainty in the imputed memberships precludes misclassification errors, potentially leading to biased results and incorrect conclusions.

Methods

We propose a strategy for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses. We exploit each individual’s typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals. Because the membership probabilities are subject to sampling variability, we propose Monte Carlo confidence intervals that explicitly acknowledge the imprecision in the estimated subgroup memberships via perturbations using a parametric bootstrap. The proposal is widely applicable and avoids stringent causal or structural assumptions that existing bias-adjustment or bias-correction methods rely on.

Results

Using two different publicly available real-world datasets, we illustrate how the proposed strategy supplements existing latent subgroup effect analyses to shed light on the potential impact of classification uncertainty on inference. First, individuals are partitioned into latent subgroups based on their medical and health history. Then within each fixed latent subgroup, the average treatment effect is assessed using an augmented inverse propensity score weighted estimator. Finally, utilizing the proposed sensitivity analysis reveals different subgroup-specific effects that are mostly insensitive to potential misclassification.

Conclusions

Our proposed sensitivity analysis is straightforward to implement, provides both graphical and numerical summaries, and readily permits assessing the sensitivity of any machine learning-based causal effect estimator to classification uncertainty. We recommend making such sensitivity analyses more routine in latent subgroup effect analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01720-8.