scholarly journals Causal mediation analysis with multiple causally non-ordered mediators

2015 ◽  
Vol 27 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Masataka Taguri ◽  
John Featherstone ◽  
Jing Cheng
Keyword(s):  
Indirect Effect ◽  
Interactive Effect ◽  
Mediation Effect ◽  
Mediation Effects ◽  
Mediation Analyses ◽  
Intermediate Variable ◽  
Causal Mediation ◽  
Health Studies ◽  
Using Data ◽  
The Individual

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

scholarly journals Variable selection for causal mediation analysis using LASSO-based methods

2021 ◽  
pp. 096228022199750
Author(s):  
Zhaoxin Ye ◽  
Yeying Zhu ◽  
Donna L Coffman
Keyword(s):  
Propensity Score ◽  
Indirect Effect ◽  
Adaptive Lasso ◽  
Mediation Effect ◽  
Regularization Methods ◽  
Causal Mediation ◽  
Outcome Information ◽  
Causal Mediation Analysis ◽  
Selection For ◽  
Mimic Iii

Causal mediation effect estimates can be obtained from marginal structural models using inverse probability weighting with appropriate weights. In order to compute weights, treatment and mediator propensity score models need to be fitted first. If the covariates are high-dimensional, parsimonious propensity score models can be developed by regularization methods including LASSO and its variants. Furthermore, in a mediation setup, more efficient direct or indirect effect estimators can be obtained by using outcome-adaptive LASSO to select variables for propensity score models by incorporating the outcome information. A simulation study is conducted to assess how different regularization methods can affect the performance of estimated natural direct and indirect effect odds ratios. Our simulation results show that regularizing propensity score models by outcome-adaptive LASSO can improve the efficiency of the natural effect estimators and by optimizing balance in the covariates, bias can be reduced in most cases. The regularization methods are then applied to MIMIC-III database, an ICU database developed by MIT.

scholarly journals PENGARUH KELEKATAN TERHADAP KESEJAHTERAAN PSIKOLOGIS YANG DIMEDIASI OLEH RESILIENSI PADA REMAJA DENGAN ORANG TUA BERCERAI

2020 ◽  
Vol 4 (1) ◽  
pp. 32-44
Author(s):  
Putri Puspitasari ◽  
Sri Maslihah ◽  
Anastasia Wulandari
Keyword(s):  
High School ◽  
Well Being ◽  
Peer Attachment ◽  
Psychological Well Being ◽  
Mediation Effects ◽  
Mediation Analyses ◽  
Causal Mediation ◽  
Other Hand ◽  
Resilience Scale

The aim of this study was to examine the correlation of attachment on psychological well-being with resilience act as mediator. Participants were 127 high school adolescents with parent divorced while aged 0 to 12 years old. Attachment was assessed by Inventory of Parent and Peer Attachment (IPPA), resilience was assessed by Resilience Scale (RS), and psychological well-being was assessed by Psychological Well-Being Scale (PWBS) instruments. Causal mediation analyses were used to examine the proposed mediation effects. Result showed that attachment can predict adolescents’ psychological well-being. On other hand, resilience functioned as a mediator on the correlation.

scholarly journals Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J Blaser ◽  
Huilin Li
Keyword(s):  
Regression Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Mediation Effect ◽  
Supplementary Information ◽  
High Dimensional ◽  
Model Framework ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Microbiome Data

Abstract Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. Availability and implementation https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Network of biomarkers and their mediation effects on the associations between regular exercise and the incidence of cardiovascular & metabolic diseases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
JooYong Park ◽  
Jaesung Choi ◽  
Ji-Eun Kim ◽  
Miyoung Lee ◽  
Aesun Shin ◽  
...  
Keyword(s):  
Linear Models ◽  
Metabolic Diseases ◽  
Mediation Effect ◽  
Regular Exercise ◽  
Key Factors ◽  
Anthropometric Measures ◽  
Mediation Effects ◽  
Men And Women ◽  
Mediation Analyses ◽  
Cox Proportional Hazard Models

AbstractThis study aimed to understand the biological process related to the prevention of cardiovascular & metabolic diseases (CMD), including diabetes, hypertension, and dyslipidemia via regular exercise. This study included 17,053 subjects aged 40–69 years in the Health Examinees Study from 2004 to 2012. Participation in regular exercise was investigated by questionnaires. Data on 42 biomarkers were collected from anthropometric measures and laboratory tests. We examined the associations between regular exercise and biomarkers using general linear models, between biomarkers and the risk of CMD using cox proportional hazard models, and the mediation effect of biomarkers using mediation analyses. Biomarker networks were constructed based on the significant differential correlations (p < 0.05) between the exercise and non-exercise groups in men and women, respectively. We observed significant mediators in 14 and 16 of the biomarkers in men and women, respectively. Triglyceride level was a noteworthy mediator in decreasing the risk of CMD with exercise, explaining 23.79% in men and 58.20% in women. The biomarker network showed comprehensive relationships and associations among exercise, biomarkers, and CMD. Body composition-related biomarkers were likely to play major roles in men, while obesity-related biomarkers seemed to be key factors in women.

scholarly journals Estimation of additive and non-additive effects on milk production traits in Iranian Holstein crossbred population

2012 ◽  
Vol 55 (6) ◽  
pp. 562-566
Author(s):  
A. Ghorbani ◽  
R. Salamatdoustnobar
Keyword(s):  
Milk Yield ◽  
Mixed Models ◽  
Interactive Effect ◽  
Production Traits ◽  
Multiple Regression Method ◽  
Negative Effect ◽  
Fat Yield ◽  
Using Data ◽  
The Individual ◽  
Recombination Effect

Abstract. Direct and interactive effect (individual and maternal heterosis) was estimated using data from rotational crosses between Holsteins with Iranian native breeds. Traits of interest were milk yield, fat yield, fat percent and milk days. Complete data were available on 155240 animals from 1991 through 2003. Direct additive’s effect, individual heterosis, maternal heterosis and recombination (interactions between presences of Holstein gene in two parents) effects were estimated by multiple regression method in SAS 8.2 with mixed models procedure. The least squares means of milk yield, fat yield, fat percent and milk days were 2722.68±1 541.12 kg, 122.97±47.40 kg, 3.97±0.73 percent and 260.10±89.51 days respectively. All direct and indirect genetic effects are significant in milk and milk days traits (P<0.05). Individual and maternal heterosis and recombination effect are not significant on fat yield and fat percent traits. The individual and recombination effect were negative effect on milk yield. The result suggested that the Holstein is a favourable breed for crossbreeding program in developing country as Iran.

scholarly journals Mediating Effects of Frailty Indicators on the Risk of Sepsis After Cancer

2018 ◽  
Vol 35 (7) ◽  
pp. 708-719 ◽  
Author(s):  
Justin Xavier Moore ◽  
Tomi Akinyemiju ◽  
Alfred Bartolucci ◽  
Henry E. Wang ◽  
John Waterbor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cancer Survivors ◽  
Racial Differences ◽  
Cancer Survivorship ◽  
Cox Regression ◽  
Mediation Effect ◽  
Cancer History ◽  
Mediation Effects ◽  
Increased Risk ◽  
Using Data

Background: Cancer survivors are at increased risk of sepsis, possibly attributed to weakened physiologic conditions. The aims of this study were to examine the mediation effect of indicators of frailty on the association between cancer survivorship and sepsis incidence and whether these differences varied by race. Methods: We performed a prospective analysis using data from the REasons for Geographic and Racial Differences in Stroke cohort from years 2003 to 2012. We categorized frailty as the presence of ≥2 frailty components (weakness, exhaustion, and low physical activity). We categorized participants as “cancer survivors” or “no cancer history” derived from self-reported responses of being diagnosed with any cancer. We examined the mediation effect of frailty on the association between cancer survivorship and sepsis incidence using Cox regression. We repeated analysis stratified by race. Results: Among 28 062 eligible participants, 2773 (9.88%) were cancer survivors and 25 289 (90.03%) were no cancer history participants. Among a total 1315 sepsis cases, cancer survivors were more likely to develop sepsis (12.66% vs 3.81%, P < .01) when compared to participants with no cancer history (hazard ratios: 2.62, 95% confidence interval: 2.31-2.98, P < .01). The mediation effects of frailty on the log-hazard scale were very small: weakness (0.57%), exhaustion (0.31%), low physical activity (0.20%), frailty (0.75%), and total number of frailty indicators (0.69%). Similar results were observed when stratified by race. Conclusion: Cancer survivors had more than a 2-fold increased risk of sepsis, and indicators of frailty contributed to less than 1% of this disparity.

scholarly journals Distinctive Mediating Effects of Subcortical Structure Changes on the Relationships Between Amyloid or Vascular Changes and Cognitive Decline

2021 ◽  
Vol 12 ◽  
Author(s):  
Na-Yeon Jung ◽  
Jeong-Hyeon Shin ◽  
Hee Jin Kim ◽  
Hyemin Jang ◽  
Seung Hwan Moon ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Indirect Effect ◽  
Rating Scale ◽  
Memory Function ◽  
Mediation Effect ◽  
Subcortical Structure ◽  
Vascular Changes ◽  
Subcortical Structures ◽  
Volume Changes ◽  
Mediation Effects

Objective: We investigated the mediation effects of subcortical volume change in the relationship of amyloid beta (Aβ) and lacune with cognitive function in patients with mild cognitive impairment (MCI).Methods: We prospectively recruited 101 patients with MCI who were followed up with neuropsychological tests, MRI, or Pittsburgh compound B (PiB) PET for 3 years. The mediation effect of subcortical structure on the association of PiB or lacunes with cognitive function was analyzed using mixed effects models.Results: Volume changes in the amygdala and hippocampus partially mediated the effect of PiB changes on memory function (direct effect = −0.168/−0.175, indirect effect = −0.081/−0.077 for amygdala/hippocampus) and completely mediated the effect of PiB changes on clinical dementia rating scale sum of the box (CDR-SOB) (indirect effect = 0.082/0.116 for amygdala/hippocampus). Volume changes in the thalamus completely mediated the effect of lacune on memory, frontal executive functions, and CDR-SOB (indirect effect = −0.037, −0.056, and 0.047, respectively).Conclusions: Our findings provide a better understanding of the distinct role of subcortical structures in the mediation of the relationships of amyloid or vascular changes with a decline in specific cognitive domains.

scholarly journals Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li
Keyword(s):  
Regression Model ◽  
Association Studies ◽  
Statistical Significance ◽  
Mediation Effect ◽  
High Dimensional ◽  
Model Framework ◽  
Mediation Effects ◽  
Microbiome Composition ◽  
Causal Mediation ◽  
Microbiome Data

AbstractMotivationRecent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.ResultsWe propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.

scholarly journals Estimation and Inference for the Mediation Effect in a Time-Varying Mediation Model

2021 ◽  
Author(s):  
Xizhen Cai ◽  
Donna Coffman ◽  
Megan Piper ◽  
Runze Li
Keyword(s):  
Indirect Effect ◽  
Mediation Analysis ◽  
Mediation Effect ◽  
Outcome Variable ◽  
Time Varying ◽  
Mediation Model ◽  
Real World Data ◽  
Mediation Effects ◽  
Simulation Based ◽  
Time Invariant

Abstract Background: Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e. the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. Methods: We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise conffidence band for making inference of the time-varying mediation effect. Results: Simulation studies show that the proposed procedures perform well when comparing the conffidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to real-world data collected from a smoking cessation study. Conclusions: We present a practical model for estimating the time-varying mediation effects to allow time-varying outcome as well as time-varying mediator. Simulation-based inference tool is also proposed and implemented an R package on CRAN.

scholarly journals Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

2020 ◽  
Author(s):  
Zhonghua Liu ◽  
Jincheng Shen ◽  
Richard Barfield ◽  
Joel Schwartz ◽  
Andrea Baccarelli ◽  
...  
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Statistical Significance ◽  
Null Distribution ◽  
Mediation Effect ◽  
Type I ◽  
Mediation Effects ◽  
Type I Error Rate ◽  
Causal Mediation ◽  
Genome Wide

In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel's test and the joint significant test are underpowered and thus can miss important scientific discoveries. In this paper, we show that the null distribution of Sobel's test is not the standard normal distribution and the null distribution of the joint significant test is not uniform under the composite null of no mediation effect, especially in finite samples and under the singular point null case that the exposure has no effect on the mediator and the mediator has no effect on the outcome. Our results clearly explain why these two tests are underpowered, and more importantly motivate us to develop a more powerful Divide-Aggregate Composite-null Test (DACT) for the composite null hypothesis of no mediation effect by leveraging epigenome-wide data. We adopted Efron's empirical null framework for assessing statistical significance. We show that the proposed DACT method has improved power, and can well control type I error rate. Our extensive simulation studies showed that the DACT method properly controls the type I error rate and outperforms Sobel's test and the joint significance test for detecting mediation effects. We applied the DACT method to the Normative Aging Study and identified additional DNA methylation CpG sites that might mediate the effect of smoking on lung function. We then performed a comprehensive sensitivity analysis to demonstrate that our mediation data analysis results were robust to unmeasured confounding. We also developed a computationally-efficient R package DACT for public use, available at https://github.com/zhonghualiu/DACT.

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