Assessing Mediational Processes Using Piecewise Linear Growth Curve Models with Individual Measurement Occasions

TL;DR

This paper introduces two piecewise linear growth curve models for longitudinal mediation analysis, allowing for the assessment of unidirectional mediational processes with accurate estimation demonstrated through simulations and real data.

Contribution

It develops novel piecewise linear growth models for longitudinal mediation, enabling estimation of unidirectional effects with validated unbiasedness and accuracy.

Findings

Models provide unbiased, accurate estimates

Simulation studies confirm target coverage probabilities

Empirical analysis demonstrates covariate effects on outcomes

Abstract

Longitudinal processes often unfold concurrently where the growth of two or more longitudinal outcomes are associated. Additionally, if the study under investigation is long, the growth curves may exhibit nonconstant change with respect to time. Multiple existing studies have developed multivariate growth models with nonlinear functional forms to explore joint development where two longitudinal records are correlated over time. However, the relationship between multiple longitudinal outcomes may also be unidirectional. Accordingly, it is of interest to estimate regression coefficients of such unidirectional paths. One statistical tool for such analyses is longitudinal mediation models. In this study, we develop two models to evaluate mediational processes where the linear-linear piecewise growth model is utilized to capture the change patterns. We define the mediational process as either the baseline covariate or the change in covariate influencing the change in the mediator, which, in turn, affects the change in the outcome. We present the proposed models through simulation studies and real-world data analyses. Our simulation studies demonstrate that the proposed mediational models can provide unbiased and accurate point estimates with target coverage probabilities with a 95% confidence interval. The empirical analyses demonstrate that the proposed model can estimate covariates' direct and indirect effects on the change in the outcome. We also provide the corresponding code for the proposed models.