Relevant bibliographies by topics / Nonlinear mediation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Nonlinear mediation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Nonlinear mediation"

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Loeys, Tom, Beatrijs Moerkerke, Olivia De Smet, Ann Buysse, Johan Steen, and Stijn Vansteelandt. "Flexible Mediation Analysis in the Presence of Nonlinear Relations: Beyond the Mediation Formula." Multivariate Behavioral Research 48, no. 6 (2013): 871–94. http://dx.doi.org/10.1080/00273171.2013.832132.

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Smith, E. Keith, Michael G. Lacy, and Adam Mayer. "Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models." Stata Journal: Promoting communications on statistics and Stata 19, no. 4 (2019): 913–30. http://dx.doi.org/10.1177/1536867x19893638.

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Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.
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Albert, Jeffrey M. "Distribution-Free Mediation Analysis for Nonlinear Models with Confounding." Epidemiology 23, no. 6 (2012): 879–88. http://dx.doi.org/10.1097/ede.0b013e31826c2bb9.

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De Vera, Luis, Rubén V. Rial, Ernesto Pereda, and Julián J. González. "Autonomic mediation of the interdependence between variability signals of heart rate and blood pressure in the lizard Gallotia galloti." Canadian Journal of Zoology 90, no. 7 (2012): 839–48. http://dx.doi.org/10.1139/z2012-052.

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Autonomic nervous system (ANS) involvement in the mediation of the synchronization between beat-to-beat RR interval variability (RRIV) and systolic blood pressure variability (SBPV) signals of the lizard Gallotia galloti (Oudart, 1839) was investigated through linear and nonlinear time series analysis methods in a pharmacological blockade context. The ANS blockers used were atropine, prazosin, and propranolol. The interdependence between the signals was quantified by means of the magnitude-squared coherence (MSC), which measures amplitude and phase linear synchronization; the phase lag index (PLI), which evaluates the phase synchronization; and the index L, which quantifies the generalized linear and nonlinear synchronization. Atropine decreased the PLI in the low-frequency (LF: 0.01–0.05 Hz) range; prazosin decreased the MSC in the medium-frequency (MF: 0.06–0.15 Hz) range; and propranolol did not alter any of the interdependence measures. It is suggested that (i) the cholinoceptor activity mediates the phase cardiovascular synchronization in the LF range; (ii) the α1-adrenoceptor activity mediates the amplitude and phase linear cardiovascular synchronization in the MF range; and (iii) the β-adrenoceptor activity plays no role in mediating any dynamics of cardiovascular synchronization in the studied frequency range. When comparing these results with those in mammals, a lesser overall autonomic involvement in the mediation of the studied cardiovascular interdependences is seen in reptiles.
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Zhang, Wenjia, Daming Lu, Yanyan Chen, and Chengcheng Liu. "Land use densification revisited: Nonlinear mediation relationships with car ownership and use." Transportation Research Part D: Transport and Environment 98 (September 2021): 102985. http://dx.doi.org/10.1016/j.trd.2021.102985.

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Geldhof, G. John, Katherine P. Anthony, James P. Selig, and Carolyn A. Mendez-Luck. "Accommodating binary and count variables in mediation." International Journal of Behavioral Development 42, no. 2 (2017): 300–308. http://dx.doi.org/10.1177/0165025417727876.

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The existence of several accessible sources has led to a proliferation of mediation models in the applied research literature. Most of these sources assume endogenous variables (e.g., M, and Y) have normally distributed residuals, precluding models of binary and/or count data. Although a growing body of literature has expanded mediation models to include more diverse data types, the nonlinearity of these models presents a substantial hurdle to their implementation and interpretation. The present study extends the existing literature (e.g., Hayes & Preacher, 2010; Stolzenberg, 1980) to propose conditional indirect effects as a useful tool for understanding mediation models that include paths estimated using the Generalized Linear Model (e.g., logistic regression, Poisson regression). We briefly review the relevant literature, culminating in a discussion of conditional indirect effects and their importance when examining nonlinear associations. We present a simple extension of the equations presented by Hayes and Preacher (2010) and provide an applied example of the technique.
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Liu, Wei, John P. Haran, Arlene S. Ash, et al. "High-dimensional Causal Mediation Analysis with a Large Number of Mediators Clumping at Zero to Assess the Contribution of the Microbiome to the Risk of Bacterial Pathogen Colonization in Older Adults." Current Bioinformatics 15, no. 7 (2020): 671–96. http://dx.doi.org/10.2174/1574893614666191115123219.

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Background: Causal mediation analysis is conducted in biomedical research with the goal of investigating causal mechanisms that consist of both direct causal pathways between the treatment and outcome variables and intermediate causal pathways through mediators. Recently, this type of analysis has been applied in the context of bioinformatics; however, it encounters the obstacle of high-dimensional and semi-continuous mediators with clumping at zero. Methods: In this article, we develop a methodology to conduct high-dimensional causal mediation analysis with a modeling framework that involves (i) a nonlinear model for the outcome variable, (ii) two-part models for semi-continuous mediators with clumping at zero, and (iii) sophisticated variable-selection techniques using machine learning. We conducted simulations and investigated the performance of the proposed method. It is shown that the proposed method can provide reliable statistical information on the causal effects with high-dimensional mediators. The method is adopted to assess the contribution of the intestinal microbiome to the risk of bacterial pathogen colonization in older adults from US nursing homes. Conclusions: The proposed high-dimensional causal mediation analysis with nonlinear models is an innovative and reliable approach to conduct causal inference with high-dimensional mediators.
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Cheng, Jing, Nancy F. Cheng, Zijian Guo, Steven Gregorich, Amid I. Ismail, and Stuart A. Gansky. "Mediation analysis for count and zero-inflated count data." Statistical Methods in Medical Research 27, no. 9 (2017): 2756–74. http://dx.doi.org/10.1177/0962280216686131.

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Different conventional and causal approaches have been proposed for mediation analysis to better understand the mechanism of a treatment. Count and zero-inflated count data occur in biomedicine, economics, and social sciences. This paper considers mediation analysis for count and zero-inflated count data under the potential outcome framework with nonlinear models. When there are post-treatment confounders which are independent of, or affected by, the treatment, we first define the direct, indirect, and total effects of our interest and then discuss various conditions under which the effects of interest can be identified. Proofs are provided for the sensitivity analysis proposed in the paper. Simulation studies show that the methods work well. We apply the methods to the Detroit Dental Health Project’s Motivational Interviewing DVD trial for the direct and indirect effects of motivational interviewing on count and zero-inflated count dental caries outcomes.
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Hayes, Andrew F., and Kristopher J. Preacher. "Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear." Multivariate Behavioral Research 45, no. 4 (2010): 627–60. http://dx.doi.org/10.1080/00273171.2010.498290.

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Bhattarai, Sanju, Birgit Tandstad, Archana Shrestha, Biraj Karmacharya, and Abhijit Sen. "Socioeconomic Status and Its Relation to Hypertension in Rural Nepal." International Journal of Hypertension 2021 (August 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/5542438.

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Introduction. Hypertension and its association with socioeconomic positions are well established. However, the gradient of these relationships and the mediating role of lifestyle factors among rural population in low- and middle-income countries such as Nepal are not fully understood. We sought to assess the association between socioeconomic factors (education, income, and employment status) and hypertension. Also, we assessed whether the effect of education and income level on hypertension was mediated by lifestyle factors. Methods. This cross-sectional study was conducted among 260 participants aged ≥18 years attending a rural health center in Dolakha, Nepal. Self-reported data on demographic, socioeconomic, and lifestyle factors were collected, and blood pressure, weight, and height were measured for all study participants. Those with systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or administrating high blood pressure-lowering medicines were regarded as hypertensives. Poisson regression models were used to estimate the prevalence ratios and corresponding 95% confidence intervals to assess the association between socioeconomic factors and hypertension. We explored mediation, using the medeff command in Stata for causal mediation analysis of nonlinear models. Results. Of the 50 hypertensive participants, sixty percent were aware of their status. The age-standardized prevalence of hypertension was two times higher for those with higher education or high-income category. Compared to low-income and unemployed groups, the prevalence ratio of hypertension was 1.33 and 2.26 times more for those belonging to the high-income and employed groups, respectively. No evidence of mediation by lifestyle factors was observed between socioeconomic status and hypertension. Conclusions. Socioeconomic positions were positively associated with hypertension prevalence in rural Nepal. Further studies using longitudinal settings are necessary to validate our findings especially in low- and middle-income countries such as Nepal.
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Dissertations / Theses on the topic "Nonlinear mediation"

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Lockhart, Lester Leland. "Nonlinear mediation in clustered data : a nonlinear multilevel mediation model." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-2733.

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Mediational analysis quantifies proposed causal mechanisms through which treatments act on outcomes. In the presence of clustered data, conventional multiple regression mediational methods break down, requiring the use of hierarchical linear modeling techniques. As an additional consideration, nonlinear relationships in multilevel mediation models require unique specifications that are ignored if modeled linearly. Improper specification of nonlinear relationships can lead to a consistently overestimated mediated effect. This has direct implications for inferences regarding intervention causality and efficacy. The current investigation proposes a nonlinear multilevel mediation model to account for nonlinear relationships in clustered data. A simulation study is proposed to compare the statistical performance of the proposed nonlinear multilevel mediation model with that of conventional methods.<br>text
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Lockhart, Lester Leland. "Nonlinear mediation in clustered data : a nonlinear multilevel mediation model." 2012. http://hdl.handle.net/2152/19599.

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Mediational analysis quantifies proposed causal mechanisms through which treatments act on outcomes. In the presence of clustered data, conventional multiple regression mediational methods break down, requiring the use of hierarchical linear modeling techniques. As an additional consideration, nonlinear relationships in multilevel mediation models require unique specifications that are ignored if modeled linearly. Improper specification of nonlinear relationships can lead to a consistently overestimated mediated effect. This has direct implications for inferences regarding intervention causality and efficacy. The current investigation examined a specific nonlinear multilevel mediation model parameterization to account for nonlinear relationships in clustered data. A simulation study was conducted to compare linear and nonlinear model specifications in the presence of truly nonlinear data. MacKinnon et al.’s (2007a) empirical-M based PRODCLIN method for estimating the confidence interval surrounding the instantaneous indirect effect was used to compare confidence interval coverage rates surrounding both the linear and nonlinear models’ estimates. Overall, the nonlinear model’s estimates were less biased, more efficient, and produced higher coverage rates than the linear model specification. For conditions containing a true value of zero for the instantaneous indirect effect, bias, efficiency, and coverage rate values were similar for the linear and nonlinear estimators. For conditions with a non-zero value for the instantaneous indirect effect, both the linear and nonlinear models were substantially biased. However, the nonlinear model was always less biased and always produced higher coverage rates than the linear model. The nonlinear model was more efficient than the linear model for all but two design conditions.<br>text
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CHUANG, SHU-HUEI, and 莊淑惠. "A Test of Moderating /Mediating and Linear / Nonlinear relationship between Quality, Satisfaction, and Loyalty." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/23413794422849938662.

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碩士<br>玄奘大學<br>國際企業學系碩士班<br>96<br>Based on existing literature, this study constructs alternative relationships between quality, satisfaction, and loyalty and tests these relationships with users of automobiles and cosmetics respectively. The purpose of this study is to clarify the confusing causal paths and linear vs. nonlinear relationships between these important constructs, and therefore, to understand the relative accuracy of alternative competing models. The results of hierarchical regression analysis indicate that: Satisfaction is a full mediator between quality and loyalty, but quality is not a mediator between satisfaction and loyalty; The moderating relationships, not matter satisfaction or quality as a moderator between the other two constructs, are statistically invalid; The relationships between quality, satisfaction, and loyalty are best described as linear, and the nonlinear relationships are statistically invalid. Overall, the results of this study support the linear sequential relationships between quality, satisfaction, and loyalty, and suggest that research design and statistical analysis artifacts may contribute to the evidences that supported the existence of moderating and nonlinear relationship.
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Book chapters on the topic "Nonlinear mediation"

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Pearl, Judea. "The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models." In Causality. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119945710.ch12.

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Hu, Die, Maoyan She, and Xue Yang. "How Does Environmental Regulation Enhance Firms’ Competitiveness Through Innovation Incentive in China? A Nonlinear Mediating Model for the Porter Hypothesis." In Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49829-0_60.

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Conference papers on the topic "Nonlinear mediation"

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Ferreri, Suzanne, and Yi-Xian Qin. "Alteration of Bone’s Nonlinear Elastic and Viscoelastic Nanomechanical Properties Is Triggered by Low Intensity Pulsed Ultrasound." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206711.

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Dynamic mechanotransduction, particularly under high frequency, low amplitude signals, has been proven effective in mediating bone loss and improving mechanical strength for tissues affected by estrogen deficient osteopenia. Ultrasound, which behaves as an alternating pressure wave in bone, may offer an effective, non-invasive technology for delivery of anabolic signals. In vitro, dynamic mechanical signals delivered using ultrasound have been shown to increase osteoblast proliferation [1], and in vivo studies have established ultrasound as an effective treatment for delayed and non-union fractures [2]. Previously, we showed that ultrasound signals similar to those currently used in a clinical setting for fracture healing were effective in mediating decreases in bone volume and mechanical strength at the millimeter length-scale in response to estrogen deficient osteopenia [3]. Due to bone’s inherent viscoelasticity and the dynamic nature of the applied ultrasound signals, it is particularly important to consider both elastic and viscous components of bone’s adaptive response to applied loads. In light of these findings, the goal of this study was to determine the role of therapeutic ultrasound signal intensity in modulating changes in bone’s nanoscale elastic and viscoelastic material properties associated with estrogen deficient osteopenia. We hypothesize that bone is sensitive to dynamic mechanical signals delivered via ultrasound and that bone’s tissue level nano-scale material properties, particularly nonlinear viscoelastic properties, are sensitive to ultrasound signal intensity.
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Reports on the topic "Nonlinear mediation"

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Pearl, Judea. The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada557435.

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