Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

Tofighi, Davood; Hsiao, Yu‐Yu; Kruger, Eric; MacKinnon, David P.; Witkiewitz, Katie

doi:10.1080/10705511.2018.1506925

Cited by 24 publications

(17 citation statements)

References 79 publications

(110 reference statements)

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“…Finally, we added household income as an exposure and school readiness skills as outcomes into the LGCMs to understand the contribution of antenatal maternal mental health (intercept) and its linear trajectory (slope) as potential mediators. As the indirect paths through two covarying latent mediators could be sensitive to omitted confounders, we conducted correlated augmented model sensitivity analysis (CAMSA), which used correlations between residuals, termed confounder correlations, to model the effects of the omitted confounders (Tofighi et al ., 2019 ). These models were completed using Mplus 8 (Los Angeles, CA: Muthén & Muthén).…”

Section: Methodsmentioning

confidence: 99%

Income disparity in school readiness and the mediating role of perinatal maternal mental health: a longitudinal birth cohort study

Law

Aishworiya

Cai

et al. 2021

Epidemiol Psychiatr Sci

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Aims There is compelling evidence for gradient effects of household income on school readiness. Potential mechanisms are described, yet the growth curve trajectory of maternal mental health in a child's early life has not been thoroughly investigated. We aimed to examine the relationships between household incomes, maternal mental health trajectories from antenatal to the postnatal period, and school readiness. Methods Prospective data from 505 mother–child dyads in a birth cohort in Singapore were used, including household income, repeated measures of maternal mental health from pregnancy to 2-years postpartum, and a range of child behavioural, socio-emotional and cognitive outcomes from 2 to 6 years of age. Antenatal mental health and its trajectory were tested as mediators in the latent growth curve models. Results Household income was a robust predictor of antenatal maternal mental health and all child outcomes. Between children from the bottom and top household income quartiles, four dimensions of school readiness skills differed by a range of 0.52 (95% Cl: 0.23, 0.67) to 1.21 s.d. (95% CI: 1.02, 1.40). Thirty-eight percent of pregnant mothers in this cohort were found to have perinatal depressive and anxiety symptoms in the subclinical and clinical ranges. Poorer school readiness skills were found in children of these mothers when compared to those of mothers with little or no symptoms. After adjustment of unmeasured confounding on the indirect effect, antenatal maternal mental health provided a robust mediating path between household income and multiple school readiness outcomes (χ2 126.05, df 63, p < 0.001; RMSEA = 0.031, CFI = 0.980, SRMR = 0.034). Conclusions Pregnant mothers with mental health symptoms, particularly those from economically-challenged households, are potential targets for intervention to level the playing field of their children.

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Section: Methodsmentioning

confidence: 99%

Income disparity in school readiness and the mediating role of perinatal maternal mental health: a longitudinal birth cohort study

Law

Aishworiya

Cai

et al. 2021

Epidemiol Psychiatr Sci

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“…The confounder correlations ρ 1 to ρ 5 are assumed to model the effect of the omitted confounder bias on the model parameters although the exact nature of the relationships between the confounder correlations and the omitted confounder remains unclear. Note that if we had assumed that all the confounders were included in the model (e.g., the covariates included the confounders), most if not all the confounder correlations would equal zero (Tofighi et al, 2013(Tofighi et al, , 2019Tofighi and Kelley, 2016).…”

Section: Camsamentioning

confidence: 99%

“…As a result, it is more challenging to assess the impact of violating the no confounding assumption because the additional patterns of confounding can happen with a nonrandomized mediation model when that model is compared to a randomized mediation model. Because the no omitted confounder assumption is not testable and because the proper randomization of the antecedent and mediator variables is absent, researchers have recommended sensitivity analysis ( Imai et al, 2010 ; VanderWeele and Arah, 2011 ; Tofighi et al, 2013 , 2019 ; Albert and Wang, 2015 ; VanderWeele, 2015 ; Tofighi and Kelley, 2016 ). A sensitivity analysis assesses the impact of various degrees of violation of the no omitted confounder assumption on the model parameter estimates and on any inferences about the indirect effects.…”

Section: Introductionmentioning

confidence: 99%

“…In multilevel/longitudinal mediation analysis, methods to conduct sensitivity analysis have been proposed for nonrandomized ( Tofighi and Kelley, 2016 ) and for randomized single-mediator model ( Bind et al, 2016 ; Talloen et al, 2016 ). For two mediators, Tofighi et al (2019) proposed an SEM-based sensitivity analysis method for a randomized LGCMM. However, Tofighi et al (2019) did not consider a nonrandomized model where a confounder can influence a pair of relationships among antecedent variable, mediators, and outcome variable.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis in Nonrandomized Longitudinal Mediation Analysis

Tofighi

2021

Front. Psychol.

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Mediation analysis relies on an untestable assumption of the no omitted confounders, which posits that an omitted variable that confounds the relationships between the antecedent, mediator, and outcome variables cannot exist. One common model in alcohol addiction studies is a nonrandomized latent growth curve mediation model (LGCMM), where the antecedent variable is not randomized, the two covarying mediators are latent intercept and slope modeling longitudinal effect of the repeated measures mediator, and an outcome variable that measures alcohol use. An important gap in the literature is lack of sensitivity analysis techniques to assess the effect of the violation of the no omitted confounder assumption in a nonrandomized LGCMM. We extend a sensitivity analysis technique, termed correlated augmented mediation sensitivity analysis (CAMSA), to a nonrandomized LGCMM. We address several unresolved issues in conducting CAMSA for the nonrandomized LGCMM and present: (a) analytical results showing how confounder correlations model a confounding bias, (b) algorithms to address admissible values for confounder correlations, (c) accessible R code within an SEM framework to conduct our proposed sensitivity analysis, and (d) an empirical example. We conclude that conducting sensitivity analysis to ascertain robustness of the mediation analysis is critical.

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“…Josephy and colleagues (2015) describe a method of sensitivity analysis for evaluating the assumptions in a withinsubject mediation analysis. For additional resources on causal inference in longitudinal mediation analysis see Tofighi, Hsiao, Kruger, MacKinnon, Van Horn, & Witkiewitz (2018), Bind, VanderWeele, Coull &Schartz (2016), andVanderWeele (2010).…”

Section: Within-subject Designsmentioning

confidence: 99%

Selecting a Within- or Between-Subject Design for Mediation: Validity, Causality, and Statistical Power

Ak¹

2020

Preprint

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Mediation analysis is a popular statistical method throughout psychology, communication, business and other behavioral sciences. However, previous research has demonstrated that that typical sample sizes are too small to have high power to detect indirect effects in between-subject designs; however, some inferential methods have higher power than others. Montoya and Hayes (2017) developed new methods for estimating and conducting inference on indirect effects in within-subject designs. For tests of means, within-subject designs boast power advantages over between-subject designs; however, this advantage has not been demonstrated for mediation analysis. Monte Carlo simulation is used to compare six inferential methods, two designs, and a broad range of sample sizes, effect sizes, and correlations among repeated measurements. The results suggest within-subject designs require about half the sample size of between-subject designs to detect indirect effects of the same size, and the effect of design is much greater than the effect of inferential method. Factors which can impact power (e.g. highly correlated measurements) are discussed, and how to determine if a within-subject design is appropriate for a given research question. MEMORE is an easy to use tool for estimating and conducting inference on indirect effects in two-condition within-subject designs.

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Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

Cited by 24 publications

References 79 publications

Income disparity in school readiness and the mediating role of perinatal maternal mental health: a longitudinal birth cohort study

Income disparity in school readiness and the mediating role of perinatal maternal mental health: a longitudinal birth cohort study

Sensitivity Analysis in Nonrandomized Longitudinal Mediation Analysis

Selecting a Within- or Between-Subject Design for Mediation: Validity, Causality, and Statistical Power

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