Causal Mediation Programs in R, M<i>plus</i>, SAS, SPSS, and Stata

Valente, Matthew J.; Rijnhart, Judith J. M.; Smyth, Heather L.; Muniz, Felix B.; MacKinnon, David P.

doi:10.1080/10705511.2020.1777133

Cited by 95 publications

(67 citation statements)

References 26 publications

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“…The uptake of causal mediation analysis can also be enhanced through the expansion of current software packages and/or the development of new software packages that facilitate causal effect estimation for a wider range of more complicated mediation models, such as models with latent variables and multiple mediator models. To date, only M plus facilitates the estimation of causal effects for mediation models with latent variables and the causal effect estimation for multiple mediator models is only supported by the Mediation and Medflex packages in R [ 23 , 25 – 27 ]. Also, the causal effect estimation for multilevel and longitudinal mediation models is limitedly supported by the currently available software packages and warrants attention in future software development [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, various causal mediation software packages have been developed that enable researchers to apply causal mediation analysis based on only a few lines of code [ 21 – 27 , 64 ]. However, it remains unclear whether the availability of these causal mediation programs has increased the uptake of causal mediation analysis in practice.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mediation analysis methods used in observational research: a scoping review and recommendations

Rijnhart

Lamp

Valente

et al. 2021

BMC Med Res Methodol

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134

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Background Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies. Methods We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method. Results We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction. Conclusions To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mediation analysis methods used in observational research: a scoping review and recommendations

Rijnhart

Lamp

Valente

et al. 2021

BMC Med Res Methodol

Self Cite

134

View full text Add to dashboard Cite

show abstract

“…Moreover, available R (R Core Team, 2021) packages implementing methods built on this framework include CMAverse (Shi et al, 2021), medflex (Steen et al, 2017b), mediation (Tingley et al, 2014), and paths (Yamamoto & Zhou, 2021). Valente et al (2020) Yamamoto (2020). These methods essentially posit the impact of an additional hypothetical unmeasured (pre-treatment) confounder on a mediator and outcome, and systematically determine the (minimum) degree to which statistical inference would differ after adjusting for the hypothetical confounder.…”

Section: Estimation and Reporting Of Mediation Analysis Resultsmentioning

confidence: 99%

Recommendations for improving causal inference of mediation analysis

Loh

Ren²

2021

Preprint

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Mediation analysis is an indispensable tool for investigating how a treatment causally affects an outcome via intermediate variables. Recently, there have been increased concerns about the validity of causal inferences in conclusions drawn using mediation analysis. However, the discussions are limited to a single mediator, and importantly, there is a lack of guidelines on substantiating causal inferences. In this article, we first provide a thorough examination of the causal assumptions underpinning mediation analysis. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators and the stringent -- yet often overlooked -- assumptions that predicate valid inference. To mitigate the risk of invalid inference, we introduce an alternative approach focusing on mediator-specific indirect effects. An appealing feature of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate assuming a (correct) causal structure among the mediators. Finally, we provide a practical guide to improve the research practice of mediation analysis. We clarify when mediation analysis is (in)appropriate; when appropriate, we recommend that researchers preregister (i) justifications of the asserted causal relations in their mediation analysis, (ii) pre-treatment confounders, and (iii) the either path- or mediator-specific indirect effects to be investigated. Confounding adjustment when estimating the preregistered indirect effects and sensitivity analyses for unmeasured confounding can fortify causal inferences in the conclusions. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

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“…Linear regression was utilized further to measure the forecast prevalence of depression over distress in type 2 patients of diabetes mellitus. The Social Science Methodological kit (SPSS-v.27 available at https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-27 accessed on 2 April 2021) was used [38]. Continuous and group variables' mean ± standard deviation, and frequencies and percentages were calculated.…”

Section: Attributes and Statistical Analysismentioning

confidence: 99%

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

Noman

Arshad

Zeeshan

et al. 2021

IJERPH

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Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.

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Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata

Cited by 95 publications

References 26 publications

Mediation analysis methods used in observational research: a scoping review and recommendations

Mediation analysis methods used in observational research: a scoping review and recommendations

Recommendations for improving causal inference of mediation analysis

An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

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