A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Event Abstract Back to Event 2nd level modelling in fMRI analysis with a clinically depressed sample - Comparisons between classical and Bayesian methods Peter Goodin1, 2*, Joseph Ciorciari1, Susan Rossell1, 2, Matt Hughes1 and Richard Nibbs1 1 Swinburne University, BPsyC, Australia 2 Monash Alfred Psychiatric Research Centre, Australia The use of the Frequentist statistics in fMRI analysis has been the mainstay of the field for the last 25 years. Several other methods have been developed as alternatives, including those based on Bayesian statistics. Bayesian analysis methods have the benefit of allowing interrelation of data through effect size and confidence thresholds, can be more robust to outlier data and may negate the need for stringent multiple comparison correction. When examining functional differences in those with a mental illness , this can lead to detection of more subtle activation that may be of importance. Depression is a disorder that despite a common theme of symptoms is also characterised by functional heterogeneities between individuals, making inferences using Frequentist methods sometimes difficult. Utilising both Frequentist and Bayesian 2nd level analysis methods offered by SPM8 , we examined differences in activation between a healthy control sample and those with major depressive disorder during a novel emotional processing n-back task. Activation using classical methods showed small differences in more posterior visual regions however this was only detectable with liberal thresholding (p = .001 uncorrected, k = 0). In contrast Bayesian analysis (y = 0, 95% confidence) uncovered widespread differences (including overlap with those areas found with the Frequentist method) in multiple regions implicated with the disorder. This suggests that using Bayesian methods in fMRI analysis with a clinical population could be extremely useful in detecting responses that may otherwise go unnoticed or under-represented in more traditional methods, however determination of effect size importance is paramount. Keywords: Depression, fMRI, working memory, Bayesian, Frequentist Conference: XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014. Presentation Type: Poster Topic: Methods Development Citation: Goodin P, Ciorciari J, Rossell S, Hughes M and Nibbs R (2015). 2nd level modelling in fMRI analysis with a clinically depressed sample - Comparisons between classical and Bayesian methods. Conference Abstract: XII International Conference on Cognitive Neuroscience (ICON-XII). doi: 10.3389/conf.fnhum.2015.217.00203 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Feb 2015; Published Online: 24 Apr 2015. * Correspondence: Mr. Peter Goodin, Swinburne University, BPsyC, Hawthorn, Australia, peter@hitiq.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Peter Goodin Joseph Ciorciari Susan Rossell Matt Hughes Richard Nibbs Google Peter Goodin Joseph Ciorciari Susan Rossell Matt Hughes Richard Nibbs Google Scholar Peter Goodin Joseph Ciorciari Susan Rossell Matt Hughes Richard Nibbs PubMed Peter Goodin Joseph Ciorciari Susan Rossell Matt Hughes Richard Nibbs Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet causal inferences from randomized experiments are especially credible because they depend on a known assignment process, not a probability model of potential outcomes. In this article, I derive a randomization-based procedure for Bayesian inference of causal effects in a finite population setting. I formally show that this procedure satisfies Bayesian analogues of unbiasedness and consistency under weak conditions on a prior distribution. Unlike existing model-based methods of Bayesian causal inference, my procedure supposes neither probability models that generate potential outcomes nor independent and identically distributed random sampling. Unlike existing randomization-based methods of Bayesian causal inference, my procedure does not suppose that potential outcomes are discrete and bounded. Consequently, researchers can reap the benefits of Bayesian inference without sacrificing the properties that make inferences from randomized experiments especially credible in the first place.

ObjectiveTo show how a simple Bayesian analysis method can be used to improve the evidence base in patient populations where recruitment and retention are challenging.MethodsA Bayesian conjugate analysis method was...

BackgroundMediation 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.MethodsWe 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.ResultsWe 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.ConclusionsTo 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.

BackgroundLogistic regression is often used for mediation analysis with a dichotomous outcome. However, previous studies showed that the indirect effect and proportion mediated are often affected by a change of scales in logistic regression models. To circumvent this, standardization has been proposed. The aim of this study was to show the relative performance of the unstandardized and standardized estimates of the indirect effect and proportion mediated based on multiple regression, structural equation modeling, and the potential outcomes framework for mediation models with a dichotomous outcome.MethodsWe compared the performance of the effect estimates yielded by the three methods using a simulation study and two real-life data examples from an observational cohort study (n = 360).ResultsLowest bias and highest efficiency were observed for the estimates from the potential outcomes framework and for the crude indirect effect ab and the proportion mediated ab/(ab + c’) based on multiple regression and SEM.ConclusionsWe advise the use of either the potential outcomes framework estimates or the ab estimate of the indirect effect and the ab/(ab + c’) estimate of the proportion mediated based on multiple regression and SEM when mediation analysis is based on logistic regression. Standardization of the coefficients prior to estimating the indirect effect and the proportion mediated may not increase the performance of these estimates.

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Investigating and quantifying the mechanisms that underlie behavior change is essential to understanding what drives effective interventions. Cigarette smoking remains a critical concern for public health, and increasing basic knowledge of smoking behavior change can directly lead to improved interventions. This series of six studies represents a comprehensive evaluation of the mechanisms of smoking behavior change with statistical mediation analysis. All studies utilized combined data from five tailored interventions based on the Transtheoretical Model (TTM) for participants in Precontemplation (PC; N = 1145), Contemplation (C; N = 1243), and Preparation (PR; N = 499). Statistical mediation models under investigation were autoregressive, three-wave models (baseline, 12 months, and 24 months) developed within each stage of change. The ten Processes of Change for Smoking were used as independent variables, Pros of Smoking, Cons of Smoking, and Situational Temptations to Smoke were used as mediators, and a behavioral smoking outcome was used as the dependent variable. Studies 1, 2, and 3 investigated single mediator models at PC, C, and PR, respectively. Across the three stages, a total of 25 single mediator models, each with different combinations of variables, demonstrated evidence of statistical mediation. Studies 4 and 5 refined, consolidated, and extended the conclusions from these single mediator models. Study 4 found evidence of statistical mediation in multiple mediator models, and study 5 found evidence of statistical mediation in models with multiple Processes of Change for Smoking, resulting in a total of 20 final models. In study 6, the final models were tested for the presence of statistical moderation. Factorial invariance techniques were utilized to evaluate differences across subgroups for age, education level, gender, race, and original study. The statistical mediation models demonstrated equivalence across subgroups, and this suggests that the models describe mediating mechanisms that are robust across demographic and study-related variables. The 20 final models, as developed in studies 1 through 5 and further validated by study 6, highlight combinations of Processes of Change and mediators that are most related to smoking outcomes. Pros, Cons, and Situational Temptations were all found to mediate smoking behavior, with different combinations of processes, for individuals in both PC and C. The most important Processes of Change for individuals in PC included Consciousness Raising, Dramatic Relief, Environmental Reevaluation, Self-Reevaluation, and Social Liberation. The most important Processes of Change for individuals in C included Counter Conditioning, Consciousness Raising, Dramatic Relief, Environmental Reevaluation, Self-Reevaluation, Social Liberation, and Stimulus

This study examined the associations between andropause symptoms, perceived stress, and sleep quality, and determined the indirect pathway of perceived stress and sleep quality in the relationship between andropause symptoms and quality of life (QoL) among middle-aged men. This cross-sectional study employed a quantitative mediation analysis design. In total, 186 middle-aged men completed questionnaires, including the Androgen Deficiency in Aging Males Scale, Perceived Stress Scale, Pittsburgh Sleep Quality Index, and World Health Organization Quality of Life. Data were analyzed using binary logistic regression, Baron and Kenny mediation analysis, and Hayes PROCESS macro. In total, 83.3% of the participants screened positive for andropause symptoms, and 58.6% reported poor sleep quality. Andropause syndrome and perceived stress were significantly associated with an increased risk of poor sleep quality (odds ratio [OR] = 6.168, 95% confidence interval [CI] = [2.013, 18.896], p = .001; OR = 1.279, 95% CI = [1.159, 1.410], p < .001). Statistical mediation analysis indicated perceived stress and poor sleep quality explained the link between andropause symptoms and QoL (B = −0.033, p < .001; B = −0.060, p < .001; B = −0.052, p < .001). Bootstrapped mediation analysis revealed two significant indirect associations between andropause symptoms and QoL: perceived stress (B = −0.861, 95% CI = [−1.210, −0.514]) and sleep quality (B = −1.566, 95% CI = [−2.054, −1.035]). These findings provide empirical evidence that acute and sustained psychosocial burdens associated with andropause symptoms contribute substantially to a decline in overall well-being and health.

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements-effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this article is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates 2 perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the article proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types-interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses. (PsycINFO Database Record

Bayesian approaches have been demonstrated as effective methods for reliability analysis of complex systems with small-amount data, which integrate prior information and sample data using Bayes’ theorem. However, there is an assumption that precise prior probability distributions are available for unknown parameters, yet these prior distributions are sometimes unavailable in practical engineering. A possible way to avoiding this assumption is to generalize Bayesian reliability analysis approach by using imprecise probability theory. In this paper, we adopt a set of imprecise Dirichlet distributions as priors to quantify uncertainty of unknown parameters and extend traditional Bayesian reliability analysis approach by introducing an imprecise Dirichlet model (IDM). When the prior information is rare, the result of imprecise Bayesian analysis method is too rough to support engineering decision-making, so we proposed an optimization model to reduce the imprecision of the new method. Spindles are crucial for machine tools and reliability data related to spindles of new-developed machine tools are often rare. We can then use the imprecise Bayesian reliability analysis method to assess its reliability. In this paper, we mainly investigate the reliability assessment of a motorized spindle to illustrate the effectiveness of the proposed method.

Mediation analysis is widely used to examine whether a third variable mediates the relationship between two variables. Latent mediation analysis extends this framework to latent variables measured through observable indicators, combining measurement models and mediation paths. Accurate inference in latent mediation analysis depends on two critical assumptions: the correct specification of the measurement model and the absence of unmeasured confounders. However, these assumptions are often violated in practice. This study estimates latent mediation models within a Bayesian framework and investigates how specification errors and prior choices affect parameter estimates through a systematic simulation study. Specifically, we examine four typical specification errors: (1) misspecified (e.g., ignore cross-loading) measurement model for the mediator, (2) no measurement (e.g., using total score) models for the mediator, (3) no measurement models for confounders, and (4) ignoring confounders. We also evaluate the influence of prior specifications (diffuse vs. weakly informative) on Bayesian inference. The simulation results show that model misspecifications significantly affect the accuracy of mediation effect estimates. Standardized total scores for mediators and confounders attenuate mediation effects, bias parameter estimates, and produce inaccurate credible intervals. Ignoring confounders results in biased estimates, mainly when the confounding effects are medium- or large-sized. Adopting accurate weakly informative priors improves parameter recovery, coverage rates, and the ability to detect the true mediation effect compared to diffuse priors.

This paper presents a Bayesian statistical analysis method for updating earthquake ground motion versus damage relationships in the form of fragility curves and for estimating confidence bounds on these fragility curves. As building damage data from past earthquakes become increasingly available, these data need to be combined with analytical fragility curves to arrive at more robust fragility curves. The Bayesian method provides a technique for updating these relationships. The parameters of analytical fragility curves are used to estimate the prior distributions of damage in the Bayesian analysis. In an earlier paper, Singhal and Kiremidjian presented a general methodology for the development of analytical motion-damage relationships in terms of seismic fragility curves and damage probability matrices. Data on building damage are used to estimate the likelihood functions. The Bayesian method for fragility analysis is illustrated by using the damage data on reinforced concrete (RC) frame buildings from the January 17, 1994 Northridge, Calif. earthquake. The uncertainties in the fragility curves are represented by confidence bounds around the median fragility curves.