scholarly journals Estimating the natural indirect effect and the mediation proportion via the product method

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Cheng ◽  
Donna Spiegelman ◽  
Fan Li
Keyword(s):  
Sample Size ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Variance Estimation ◽  
Delta Method ◽  
Binary Outcome ◽  
Finite Sample ◽  
Data Types ◽  
Product Method ◽  
Interval Estimator

Abstract Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package is developed to implement the methods for point and variance estimation discussed in this paper.

scholarly journals Exact parametric causal mediation analysis for a binary outcome with a binary mediator

2021 ◽  
Author(s):  
Marco Doretti ◽  
Martina Raggi ◽  
Elena Stanghellini
Keyword(s):  
Logistic Regression ◽  
Direct Effect ◽  
Mediation Analysis ◽  
Delta Method ◽  
Binary Outcome ◽  
Effect Decomposition ◽  
Causal Mediation ◽  
Regression Parameters ◽  
Causal Mediation Analysis ◽  
Parametric Expression

AbstractWith reference to causal mediation analysis, a parametric expression for natural direct and indirect effects is derived for the setting of a binary outcome with a binary mediator, both modelled via a logistic regression. The proposed effect decomposition operates on the odds ratio scale and does not require the outcome to be rare. It generalizes the existing ones, allowing for interactions between both the exposure and the mediator and the confounding covariates. The derived parametric formulae are flexible, in that they readily adapt to the two different natural effect decompositions defined in the mediation literature. In parallel with results derived under the rare outcome assumption, they also outline the relationship between the causal effects and the correspondent pathway-specific logistic regression parameters, isolating the controlled direct effect in the natural direct effect expressions. Formulae for standard errors, obtained via the delta method, are also given. An empirical application to data coming from a microfinance experiment performed in Bosnia and Herzegovina is illustrated.

Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

2019 ◽  
Author(s):  
Amanda Kay Montoya ◽  
Andrew F. Hayes
Keyword(s):  
Confidence Intervals ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Analytic Approach ◽  
Analytic Framework ◽  
Hypothesis Tests ◽  
Bootstrap Confidence Intervals ◽  
Complex Models ◽  
Path Analytic ◽  
Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

scholarly journals An approximate point-based alternative for the estimation of variance under big BAF sampling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H. Gove ◽  
Timothy G. Gregoire ◽  
Mark J. Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Volume Estimation ◽  
Delta Method ◽  
Sampling Point ◽  
Variance Estimator ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula. Results Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of $\frac {1}{n}$ 1 n where n is the number of sample points.

scholarly journals Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Minimum Sample

scholarly journals Acculturation, Communication Competence, and Family Functioning in Mexican–American Mother–Daughter Dyads

2021 ◽  
Author(s):  
Becky Marquez ◽  
Tanya Benitez ◽  
Zephon Lister
Keyword(s):  
Family Functioning ◽  
Relationship Quality ◽  
Communication Skills ◽  
Adult Children ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Mexican American ◽  
Communication Competence ◽  
Well Being ◽  
Actor Partner Interdependence Model

AbstractLittle is known of how intergenerational acculturation discrepancy relates to communication skills differences that may influence relationship quality among parents and adult children. Mexican–American mother–daughter dyads (n = 59) were studied using the Actor Partner Interdependence Model to examine dyadic associations of acculturation and communication competence with family functioning and mediation analysis to determine the indirect effect of acculturation discrepancy on family functioning through communication competence differences. Communication competence of mothers exerted significant actor and partner effects on daughter-perceived cohesion and closeness. Higher acculturation discrepancy predicted greater communication competence difference which in turn was associated with lower cohesion and closeness. There was a significant indirect effect of acculturation discrepancy on daughter-perceived cohesion through communication competence difference. Communication competence of mothers impacts their own as well as their daughters’ perceptions of dyad cohesion and closeness. Intergenerational discrepant acculturation contributes to discordant communication skills that impair family functioning, which has implications for psychological well-being.

xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

2020 ◽  
Vol 20 (2) ◽  
pp. 363-381 ◽  
Author(s):  
John A. Gallis ◽  
Fan Li ◽  
Elizabeth L. Turner
Keyword(s):  
Randomized Trials ◽  
Variance Estimation ◽  
Public Health Education ◽  
Standard Errors ◽  
Future Research ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Finite Sample ◽  
Individual Level ◽  
Cluster Randomized

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

scholarly journals DAMPAK BPNT TERHADAP KONSUMSI RUMAH TANGGA DAN POLA KONSUMSI RUMAH TANGGA PENERIMA BPNT, KECAMATAN MENGWI

2021 ◽  
pp. 839
Author(s):  
Luh Ade Yumita Handriani ◽  
Sudarsana Arka
Keyword(s):  
Path Analysis ◽  
Sample Size ◽  
Direct Effect ◽  
Indirect Effect ◽  
Household Consumption ◽  
Consumption Patterns ◽  
Consumption Pattern ◽  
Large Sample Size ◽  
Analysis Techniques ◽  
The Impact

This study aims to analyze the impact of the BPNT program on household consumption and consumption patterns of BPNT recipient households in Mengwi District, Badung Regency. This research was conducted in Mengwi District, Badung Regency using a questionnaire distributed to respondents with a large sample size of 96 KPM. This study uses path analysis techniques to analyze the direct effect and Sobel test to analyze the indirect effect. Based on path analysis, the results of the study concluded that the BPNT variable had a positive and significant effect on the consumption of BPNT recipient households in Mengwi District, Badung Regency. The BPNT variable has no effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable has a negative and significant effect on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency. The household consumption variable did mediate the effect of the BPNT Program on the consumption pattern of BPNT recipient households in Mengwi District, Badung Regency

scholarly journals Smartphone Addiction, Narcissism and Aggression: A Mediation Analysis Method

2019 ◽  
Author(s):  
Anjelica Simsek ◽  
Cahit Nuri ◽  
Cemaliye Direktor ◽  
Ahmet Arnavut
Keyword(s):  
Direct Effect ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Total Effect ◽  
Mediation Effect ◽  
Smartphone Addiction ◽  
Analysis Method ◽  
Variable Effect ◽  
Independent Variable ◽  
Mediator Variable

<p>At this study, meditation effect of aggression was analyzed using Baron and Kenny’s mediation analysis method. Baron and Kenny (1986) indicates that to analyze the effect of mediator variable 3 criteria have to be actualized:</p> <p>1. Independent variable have a significant effect on a mediator variable (way a)</p> <p>2. Mediator variable have a significant effect on a dependent variable (way b)</p> <p>3. Independent variable have a significant effect on a dependent variable (way c)</p> <p>PROCESS program were used the meditational effect, it is an extra macro that is downloading to the Daniel and Hayes’s (2016) SPSS program. In this program mediation effect could be evaluated as; total effect, direct effect and indirect effect scores of mediation variable effect on dependent variable (Preacher & Hayes, 2008).</p>

Trust, ethical climate and nurses’ turnover intention

2020 ◽  
pp. 096973302096485
Author(s):  
Aditya Simha ◽  
Jatin Pandey
Keyword(s):  
Regression Analysis ◽  
Confidence Interval ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Turnover Intention ◽  
Ethical Climate ◽  
Hierarchical Regression ◽  
Bootstrap Confidence Interval ◽  
Ethical Climates ◽  
Upper Level

Background: Nursing turnover is a very serious problem, and nursing managers need to be aware of how ethical climates are associated with turnover intention. Objectives: The article explored the effects of ethical climates on nurses’ turnover intention, mediated through trust in their organization. Methods: A cross-sectional survey of 285 nurses from three Indian hospitals was conducted to test the research model. Various established Likert-type scales were used to measure ethical climates, turnover intention and trust in organization. Hierarchical regression analysis and mediation analysis were used to test the model. Results: Hierarchical regression analysis and mediation analysis were used to test the model. The indirect effect of benevolent ethical climate on turnover intention through trust in organization was –0.20 with a 95% bootstrap confidence interval of lower level = –0.31 and upper level = –0.01. The indirect effect of principled ethical climate on turnover intention through trust in organization was –0.39 with a 95% bootstrap confidence interval of lower level = –0.58 and upper level = –0.17. Ethical considerations: The study adheres to the ethical standards recommended by the American Psychological Association for conducting research with informed consent, confidentiality and privacy. Conclusion: Both benevolent and principled ethical climates decreased turnover intention indirectly through trust in organization. Only principled ethical climates were directly associated with turnover intention. Our results suggest that nurse managers and leaders should try and establish principled and benevolent climates in order to engender trust in organization and to reduce turnover intention.

scholarly journals Outcome assessment by central adjudicators in randomised stroke trials: Simulation of differential and non-differential misclassification

2020 ◽  
Vol 5 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Peter J Godolphin ◽  
Philip M Bath ◽  
Christopher Partlett ◽  
Eivind Berge ◽  
Martin M Brown ◽  
...  
Keyword(s):  
Sample Size ◽  
Outcome Assessment ◽  
Treatment Effect ◽  
Primary Outcome ◽  
Event Rate ◽  
Considerable Proportion ◽  
Binary Outcome ◽  
Primary Trial ◽  
Differential Misclassification ◽  
Larger Sample

Introduction Adjudication of the primary outcome in randomised trials is thought to control misclassification. We investigated the amount of misclassification needed before adjudication changed the primary trial results. Patients (or materials) and methods: We included data from five randomised stroke trials. Differential misclassification was introduced for each primary outcome until the estimated treatment effect was altered. This was simulated 1000 times. We calculated the between-simulation mean proportion of participants that needed to be differentially misclassified to alter the treatment effect. In addition, we simulated hypothetical trials with a binary outcome and varying sample size (1000–10,000), overall event rate (10%–50%) and treatment effect (0.67–0.90). We introduced non-differential misclassification until the treatment effect was non-significant at 5% level. Results For the five trials, the range of unweighted kappa values were reduced from 0.89–0.97 to 0.65–0.85 before the treatment effect was altered. This corresponded to 2.1%–6% of participants misclassified differentially for trials with a binary outcome. For the hypothetical trials, those with a larger sample size, stronger treatment effect and overall event rate closer to 50% needed a higher proportion of events non-differentially misclassified before the treatment effect became non-significant. Discussion: We found that only a small amount of differential misclassification was required before adjudication altered the primary trial results, whereas a considerable proportion of participants needed to be misclassified non-differentially before adjudication changed trial conclusions. Given that differential misclassification should not occur in trials with sufficient blinding, these results suggest that central adjudication is of most use in studies with unblinded outcome assessment. Conclusion: For trials without adequate blinding, central adjudication is vital to control for differential misclassification. However, for large blinded trials, adjudication is of less importance and may not be necessary.

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