Simulation-based sensitivity analysis for causal mediation studies.

Qin, Xu; Yang, Fan

doi:10.1037/met0000340

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…It is therefore judicious to carry out a sensitivity analysis to hidden confounding of the mediator-outcome relations. Methods for a single mediator include Cox et al (2013), Fritz et al (2016, Hong et al (2018), Imai, Keele, and Yamamoto (2010), Liu and Wang (2021), Yang (2021), andSmith andVanderWeele (2019); methods for multiple mediators have recently been developed by Park and Esterling (2021).…”

Section: Discussionmentioning

confidence: 99%

Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis

Loh

Ren

2023

Perspect Psychol Sci

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Mediation analysis prevails for researchers probing the etiological mechanisms through which treatment affects an outcome. A central challenge of mediation analysis is justifying sufficient baseline covariates that meet the causal assumption of no unmeasured confounding. But current practices routinely overlook this assumption. In this article, we suggest a relatively easy way to mitigate the risks of incorrect inferences resulting from unmeasured confounding: include pretreatment measurements of the mediator(s) and the outcome as baseline covariates. We explain why adjusting for pretreatment baseline measurements is a necessary first step toward eliminating confounding biases. We hope that such a practice can encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis toward improving the validity of causal inferences in psychology research.

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Section: Discussionmentioning

confidence: 99%

Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis

Loh

Ren

2023

Perspect Psychol Sci

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“…Although it is one of the predominant methodologies in the social sciences to test for mediation (Sarstedt et al, 2020 ), our use of Hayes PROCESS (Hayes, 2022 ) could not eliminate a limitation that arises with mediation models in general. That is, given the mediator and dependent variable are measured, as opposed to manipulated, the causal relationship between the mediator and the dependent variable cannot be fully established (Hayes, 2022 ; Qin & Yang, 2021 ), even with other statistical approaches to mediation like causal mediation. Since we did not include any confounding variables in Experiment 1B, we were unable to run a sensitivity analysis with our data to understand the extent to which confounding variables affect the relationship between the mediator and the dependent variable.…”

Section: Discussionmentioning

confidence: 99%

Section: Future Researchmentioning

confidence: 99%

The impact of loneliness on compliance with COVID‐19 prevention guidelines

Schultz

Newman

2022

Int J Consumer Studies

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Many individuals have been reluctant to follow the COVID‐19 prevention guidelines (e.g., wearing a mask, physical distancing, and vigilant handwashing) set forth by the U.S. Center for Disease Control to reduce the spread of COVID‐19. In this research, we use reciprocal altruism theory to investigate the role of loneliness and its impact on compliance with these guidelines. Our findings indicate that lonely individuals are less willing to comply with COVID‐19 prevention guidelines than non‐lonely individuals. Process evidence suggests that this occurs as loneliness can inhibit an individual's sense of obligation to reciprocate to others. However, we demonstrate that framing information about COVID‐19 through agentic (vs. communal) advertising messaging strategies can offset the negative impact of loneliness on compliance with COVID‐19 prevention guidelines. Thus, marketers and policymakers may want to consider the important role of loneliness when tailoring messaging appeals that encourage compliance with COVID‐19 prevention guidelines.

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“…We obtained a plausible range of bias due to omitting a pre-exposure confounder that is comparable to the observed ones. In future research, a rigorous sensitivity analysis strategy can be developed for 4-way decomposition analysis by extending an intuitive simulation-based sensitivity analysis method for 2-way decomposition analysis ( 38 ). It will allow one to (1) test for conditional associations of an unmeasured pre-exposure confounder with the exposure, mediator, and outcome, (2) simulate the confounder from its conditional distribution, and (3) assess its influence by comparing the five effect estimates before and after adjusting for it in the analysis.…”

Section: Discussionmentioning

confidence: 99%

Application of 4-way decomposition to the analysis of placental-fetal biomarkers as intermediary variables between maternal body mass index and birthweight

Xun

Qin²,

Layden³

et al. 2022

Front. Reprod. Health

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Human chorionic gonadotropin (hCG) is a placental hormone measured in pregnancy to predict individual level risk of fetal aneuploidy and other complications; yet may be useful in understanding placental origins of child development more generally. hCG was associated with maternal body mass index (BMI) and with birthweight. The primary aim here was to evaluate hCG as a mediator of maternal BMI effects on birthweight by causal mediation analysis. Subjects were 356 women from 3 U.S. sites (2010–2013). The 4-way decomposition method using med4way (STATA) was applied to screen for 5 types of effects of first trimester maternal BMI on birthweight: the total effect, the direct effect, mediation by hCG, additive interaction of BMI and hCG, and mediation in the presence of an additive interaction. Effect modification by fetal sex was evaluated, and a sensitivity analysis was performed to evaluate the assumption of unmeasured confounding. Additional placental-fetal biomarkers [pregnancy associated plasma protein A (PAPPA), second trimester hCG, inhibin-A, estriol, alpha fetoprotein] were analyzed for comparison. For first trimester hCG, there was a 0.20 standard deviation increase in birthweight at the 75th vs. 25th percentile of maternal BMI (95% CI 0.04, 0.36). Once stratified, the direct effect association was null in women carrying females. In women carrying males, hCG did not mediate the relationship. In women carrying females, there was a mediated effect of maternal BMI on birthweight by hCG in the reverse direction (−0.06, 95% CI: −0.12, 0.01), and a mediated interaction in the positive direction (0.06, 95% CI 0.00, 0.13). In women carrying males, the maternal BMI effect on birthweight was reverse mediated by PAPPA (−0.09, 95% CI: −0.17, 0.00). Sex-specific mediation was mostly present in the first trimester. Second trimester AFP was a positive mediator of maternal BMI effects in male infants only (0.06, 95% CI: −0.01, 0.13). Effect estimates were robust to potential bias due to unmeasured confounders. These findings motivate research to consider first trimester placental biomarkers and sex-specific mechanisms when quantifying the effects of maternal adiposity on fetal growth.

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Simulation-based sensitivity analysis for causal mediation studies.

Cited by 14 publications

References 40 publications

Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis

Adjusting for Baseline Measurements of the Mediators and Outcome as a First Step Toward Eliminating Confounding Biases in Mediation Analysis

The impact of loneliness on compliance with COVID‐19 prevention guidelines

Application of 4-way decomposition to the analysis of placental-fetal biomarkers as intermediary variables between maternal body mass index and birthweight

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