Integrated multiple mediation analysis: A robustness–specificity trade-off in causal structure

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when cross-world exchangeability is invalid. Consequently, this study yields a robustness–specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer data set from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality

Polygenic modelling of treatment effect heterogeneity.

Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment-mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene-gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low-density lipoprotein cholesterol

The varieties of vitality: A cross-cultural lexical analysis

Vitality has been underappreciated and underexplored by academia at large. This oversight is potentially explained by the Western-centric nature of most fields, with vitality having been comparatively neglected in the West relative to elsewhere. One explanation for this lacuna is that vitality is not easily pigeonholed within the ontological categories dominant in the West, such as mind and body. This paper therefore aims to learn from cultures that have cultivated a greater understanding of vitality, doing so by engaging with relevant ‘untranslatable’ words (i.e., those without exact equivalent in English), thus enriching our conceptual map of this topic. Over 200 relevant terms were located and analyzed using an adapted form of grounded theory. Three themes were identified, each with four subthemes: spirit (life force, channels, soul, and transcendence); energy (fortitude, channeling, willpower, and recharging); and heart (desire, passion, affection, and satisfaction). The paper thus refines our understanding of this important topic and provides a foundation for future research.

Performance of methods to conduct mediation analysis with time-to-event outcomes

Previous studies have discouraged the use of the Cox proportional hazards (PH) model for traditional mediation analysis as it might provide biased results. Accelerated failure time (AFT) models have been proposed as an alternative for Cox PH models. In addition, the use of the potential outcomes framework has been proposed for mediation models with time-to-event outcomes. The aim of this paper is to investigate the performance of traditional mediation analysis and potential outcomes mediation analysis based on both the Cox PH and the AFT model. This is done by means of a Monte Carlo simulation study and the illustration of the methods using an empirical data set. Both the product-of-coefficients method of the traditional mediation analysis and the potential outcomes framework yield unbiased estimates with respect to their own underlying indirect effect value for simple mediation models with a time-to-event outcome and estimated based on Cox PH or AFT

Prospective associations between social connectedness and mental health : evidence from a longitudinal survey and health insurance claims data

Objectives: Evidence on social stimuli associated with mental health is based mostly on self-reported health measures. We aimed to examine prospective associations between social connectedness and clinical diagnosis of depression and of anxiety. Methods: Longitudinal observational data merged with health insurance data comprising medical information on diagnosis of depression and anxiety were used. 1,209 randomly sampled employees of a US employer provided data for the analysis. Robust Poisson regression models were used. Multiple imputation was conducted to handle missing data on covariates. Results: Better social connectedness was associated with lower risks of subsequently diagnosed depression and anxiety, over a one-year follow-up period. Reports of feeling lonely were associated with increased risks of depression and anxiety. Association between community-related social connectedness and subsequent diagnosis of depression, but not of anxiety, was found. The associations were independent of demographics, socioeconomic status, lifestyle, and work characteristics. They were also robust to unmeasured confounding, missing data patterns, and prior health conditions. Conclusion: Social connectedness may be an important factor for reducing risks of depression and anxiety. Loneliness should be perceived as a risk factor for depression and anxiety

Psychological caring climate at work, mental health, well-being, and work-related outcomes : evidence from a longitudinal study and health insurance data

Psychological climate for caring (PCC) is a psychosocial factor associated with individual work outcomes and employee well-being. Evidence on the impacts of various psychological climates at work is based mostly on self-reported health measures and cross-sectional data. We provide longitudinal evidence on the associations of PCC with subsequent diagnosed depression and anxiety, subjective well-being, and self-reported work outcomes. Employees of a US organization with a worker well-being program provided data for the analysis. Longitudinal survey data merged with data from personnel files and health insurance claims records comprising medical information on diagnosis of depression and anxiety were used to regress each outcome on PCC at baseline, adjusting for prior values of all outcomes and other covariates. PCC was found to be associated with lower odds of subsequent diagnosed depression, an increase in overall well-being, mental health, physical health, social connectedness, and financial security, as well as a decrease in distraction at work, an increase in productivity/engagement and possibly in job satisfaction. There was little evidence of associations between PCC and subsequent diagnosed anxiety, character strengths, and work-family conflict. Work policies focused on improving PCC may create a promising pathway to promoting employee health and well-being as well as improving work-related outcomes

Associations of online religious participation during COVID-19 lockdown with subsequent health and well-being among UK adults.

Background In-person religious service attendance has been linked to favorable health and well-being outcomes. However, little research has examined whether online religious participation improves these outcomes, especially when in-person attendance is suspended. Methods Using longitudinal data of 8951 UK adults, this study prospectively examined the association between frequency of online religious participation during the stringent lockdown in the UK (23 March –13 May 2020) and 21 indicators of psychological well-being, social well-being, pro-social/altruistic behaviors, psychological distress, and health behaviors. All analyses adjusted for baseline socio-demographic characteristics, pre-pandemic in-person religious service attendance, and prior values of the outcome variables whenever data were available. Bonferroni correction was used to correct for multiple testing. Results Individuals with online religious participation of ≥1/week (v. those with no participation at all) during the lockdown had a lower prevalence of thoughts of self-harm in week 20 (odds ratio 0.24; 95% CI 0.09–0.62). Online religious participation of <1/week (v. no participation) was associated with higher life satisfaction (standardized β = 0.25; 0.11–0.39) and happiness (standardized β = 0.25; 0.08–0.42). However, there was little evidence for the associations between online religious participation and all other outcomes (e.g. depressive symptoms and anxiety). Conclusions There was evidence that online religious participation during the lockdown was associated with some subsequent health and well-being outcomes. Future studies should examine mechanisms underlying the inconsistent results for online v. in-person religious service attendance and also use data from non-pandemic situations

Estimating measures of interaction on an additive scale for preventive exposures

Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: −0.30; 0.82], AP = 0.30 [95%CI: −0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = −0.37 [95%CI: −1.23; 0.49], AP = −0.29 [95%CI: −0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding

Reducing bias through directed acyclic graphs

<p>Abstract</p> <p>Background</p> <p>The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.</p> <p>Discussion</p> <p>The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.</p> <p>Summary</p> <p>Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.</p