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

Survival mediation analysis with the death-truncated mediator: The completeness of the survival mediation parameter

In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well-defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We proposed three approaches to redefining the natural direct and indirect effects, which are generalized forms of the conventional causal effects for survival outcomes. Furthermore, we developed three statistical methods for the binary outcome of the survival status and formulated a Cox model for survival time. We performed simulations to demonstrate that the proposed methods are unbiased and robust. We also applied the proposed method to explore the effect of hepatitis C virus infection on mortality, as mediated through hepatitis B viral load

Association between religious service attendance and lower suicide rates among US women

IMPORTANCE: Previous studies have linked suicide risk with religious participation, but the majority have used ecologic, cross-sectional, or case-control data. OBJECTIVE: To examine the longitudinal association between religious service at tendance and suicide and the joint associations of suicide with service attendance and religious affiliation. DESIGN, SETTING, AND PARTICIPANTS: We evaluated associations between religious service attendance and suicide from 1996 through June 2010 in a large, long-term prospective cohort, the Nurses' Health Study, in an analysis that included 89 708 women. Religious service attendance was self-reported in 1992 and 1996. Data analysis was conducted from 1996 through 2010. MAIN OUTCOMES AND MEASURES: Cox proportional hazards regression models were used to examine the association between religious service attendance and suicide, adjusting for demographic covariates, lifestyle factors, medical history, depressive symptoms, and social integration measures. We performed sensitivity analyses to examine the influence of unmeasured confounding. RESULTS: Among 89 708 women aged 30 to 55 years who participated in the Nurses' Health Study, attendance at religious services once per week or more was associated with an approximately 5-fold lower rate of suicide compared with never attending religious services (hazard ratio, 0.16; 95% CI, 0.06-0.46). Service attendance once or more per week vs less frequent attendance was associated with a hazard ratio of 0.05 (95% CI, 0.006-0.48) for Catholics but only 0.34 (95% CI, 0.10-1.10) for Protestants (P = .05 for heterogeneity). Results were robust in sensitivity analysis and to exclusions of persons who were previously depressed or had a history of cancer or cardiovascular disease. There was evidence that social integration, depressive symptoms, and alcohol consumption partially mediated the association among those occasionally attending services, but not for those attending frequently. CONCLUSIONS AND RELEVANCE: In this cohort of US women, frequent religious service attendance was associated with a significantly lower rate of suicide

How to report E-values for meta-analyses: Recommended improvements and additions to the new GRADE approach.

In a recent concept paper (Verbeek et al., 2021), the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group provides a preliminary proposal to improve its existing guidelines for assessing sensitivity to uncontrolled confounding in meta-analyses of nonrandomized studies. The new proposal centers on reporting the E-value for the meta-analytic mean and on comparing this E-value to a measured "reference confounder" to determine whether residual uncontrolled confounding in the meta-analyzed studies could or could not plausibly explain away the meta-analytic mean. Although we agree that E-value analogs for meta-analyses could be an informative addition to future GRADE guidelines, we suggest improvements to the Verbeek et al. (2021)'s specific proposal regarding: (1) their interpretation of comparisons between the E-value and the strengths of associations of a reference confounder; (2) their characterization of evidence strength in meta-analyses in terms of only the meta-analytic mean; and (3) the possibility of confounding bias that is heterogeneous across studies

Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials.

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator-outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator-outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator-outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator-outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator-outcome relationship over time increased precision.Funding for the PACE trial was provided by the Medical Research Council, Department for Health for England, The Scottish Chief Scientist Office, and the Department for Work and Pensions. TC, AP and KG were in part supported by the National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, Psychology & Neuroscience, Kings College London. KG was also funded by an NIHR Doctoral Fellowship, DRF-2011-04-06

Suicide ideation of individuals in online social networks

Suicide explains the largest number of death tolls among Japanese adolescents in their twenties and thirties. Suicide is also a major cause of death for adolescents in many other countries. Although social isolation has been implicated to influence the tendency to suicidal behavior, the impact of social isolation on suicide in the context of explicit social networks of individuals is scarcely explored. To address this question, we examined a large data set obtained from a social networking service dominant in Japan. The social network is composed of a set of friendship ties between pairs of users created by mutual endorsement. We carried out the logistic regression to identify users' characteristics, both related and unrelated to social networks, which contribute to suicide ideation. We defined suicide ideation of a user as the membership to at least one active user-defined community related to suicide. We found that the number of communities to which a user belongs to, the intransitivity (i.e., paucity of triangles including the user), and the fraction of suicidal neighbors in the social network, contributed the most to suicide ideation in this order. Other characteristics including the age and gender contributed little to suicide ideation. We also found qualitatively the same results for depressive symptoms.Comment: 4 figures, 9 table

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