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Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, and empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real-world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process., (© 2024 John Wiley & Sons Ltd.)

Causal mediation analysis is advantageous for mechanism investigation. In settings with multiple causally ordered mediators, path-specific effects have been introduced to specify the effects of certain combinations of mediators. However, most path-specific effects are unidentifiable. An interventional analog of path-specific effects is adapted to address the non-identifiability problem. Moreover, previous studies only focused on cases with two or three mediators due to the complexity of the mediation formula in a large number of mediators. In this study, we provide a generalized definition of traditional path-specific effects and interventional path-specific effects with a recursive formula, along with the required assumptions for nonparametric identification. Subsequently, a general approach is developed with an arbitrary number of multiple ordered mediators and with time-varying confounders. All methods and software proposed in this study contribute to comprehensively decomposing a causal effect confirmed by data science and help disentangling causal mechanisms in the presence of complicated causal structures among multiple mediators.

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at, http://links.lww.com/EDE/B988 ., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator-outcome confounding and intermediate mediator-outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, based on a causal interpretation. Moreover, the empirical expressions of the SDE and SIE are derived with and without an intermediate mediator-outcome confounder. Then, a multiply robust estimation method is derived to mitigate the model misspecification problem. We prove that the proposed estimator is consistent, asymptotically normal, and achieves the semiparametric efficiency bound. As an illustration, we apply the proposed method to genomic datasets of lung cancer to investigate the potential role of the epidermal growth factor receptor in the treatment of lung cancer., (© 2022 John Wiley & Sons Ltd.)

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 propose a novel approach to redefining natural direct and indirect effects, which are generalized forms of conventional causal effects for survival outcomes. Furthermore, we developed three statistical methods for the binary outcome of 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., (© 2021 John Wiley & Sons Ltd.)

Path-specific effects are a critical measure for assessing mediation in the presence of multiple mediators. However, the conventional definition of path-specific effects has generated controversy because it often causes misinterpretation of the results of multiple mediator analysis. For in-depth analysis of this issue, we propose the concept of decomposing fully mediated interaction from the average causal effect. We show that misclassification of fully mediated interaction is the main cause of misinterpretation of path-specific effects. We propose two strategies for specifying fully mediated interaction: isolating and reclassifying fully mediated interaction. The choice of strategy depends on the objective. Isolating fully mediated interaction is the superior strategy when the main objective is elucidating the mediation mechanism, whereas reclassifying it is superior when the main objective is precisely interpreting the mediation analysis results. To compare performance, this study used the two proposed strategies and the conventional decomposition strategy to analyze the mediating roles of dyspnea and anxiety in the effect of impaired lung function on poor health status in a population of patients with chronic obstructive pulmonary disease. The estimation result showed that the conventional decomposition strategy underestimates the importance of dyspnea as a mechanism of this disease. Specifically, the strategy of reclassifying fully mediated interaction revealed that 50% of the average causal effect is attributable to mediating effects, particularly the mediating effect of dyspnea. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Could the direct contribution of genetic variants to the pathophysiology of uterine fibroids and the contribution mediated by age at menarche be different? SUMMARY ANSWER Age at menarche plays a mediation role in the genetic influence on uterine fibroids, and four causal genetic mechanisms underlying the age at menarche-mediated effects of common genetic loci on uterine fibroid development were identified. WHAT IS KNOWN ALREADY Uterine fibroids are common benign tumors developing from uterine smooth muscle. Genome-wide association studies (GWASs) have identified over 30 genetic loci associated with uterine fibroids in different ethnic populations. Several genetic variations in or nearby these identified loci were also associated with early age at menarche, one of the major risk factors of uterine fibroids. Although the results of GWASs reveal how genetic variations affect uterine fibroids, the genetic mechanism of uterine fibroids mediated by age at menarche remains elusive. STUDY DESIGN, SIZE, DURATION In this study, we conducted a genome-wide causal mediation analysis in two cohorts covering a total of 69 552 females of Han Chinese descent from the Taiwan Biobank (TWB). TWB is an ongoing community- and hospital-based cohort aiming to enroll 200 000 individuals from the general Taiwanese population between 30 and 70 years old. It has been enrolling Taiwanese study participants since 2012 and has extensive phenotypic data collected from 148 291 individuals as of May 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS We recruited individuals in two cohorts, with 13 899 females in TWB1 and 55 653 females in TWB2. The two sets of individuals are almost distinct, with only 730 individuals enrolled in both cohorts. Over 99% of the participants are Han Chinese. Approximately 21% of participants developed uterine fibroids. DNA samples from both cohorts were genotyped using two different customized chips (TWB1 and TWB2 arrays). After quality control and genotype imputation, 646 973 TWB1 single-nucleotide polymorphisms (SNPs) and 686 439 TWB2 SNPs were assessed in our analysis. There were 99 939 SNPs which overlapped between the TWB1 and TWB2 arrays, 547 034 TWB1 array-specific SNPs and 586 500 TWB2 array-specific SNPs. We performed GWASs for screening potential risk SNPs for age at menarche and for uterine fibroids. We subsequently identified causal mediation effects of risk SNPs on uterine fibroids mediated by age at menarche. MAIN RESULTS AND THE ROLE OF CHANCE In addition to known loci at LIN28B associated with age at menarche and loci at WNT4 associated with uterine fibroids, we identified 162 SNPs in 77 transcripts that were associated with menarche-mediated causal effects on uterine fibroids via four different causal genetic mechanisms: a both-harmful group with 52 SNPs, a both-protective group with 34 SNPs, a mediator-harmful group with 22 SNPs and a mediator-protective group with 54 SNPs. Among these SNPs, rs809302 in SLK significantly increased the risk of developing uterine fibroids by 3.92% through a mechanism other than age at menarche (P  <   10−10), and rs371721345 in HLA-DOB was associated with a 2.70% decreased risk (P  <   10−10) in the occurrence of uterine fibroids, mediated by age at menarche. These findings provide insights into the mechanism underlying the effect of genetic loci on uterine fibroids mediated by age at menarche. LIMITATIONS, REASONS FOR CAUTION A potential issue is that the present study relied upon self-reported age at menarche and uterine fibroid information. Due to the experimental design, the consistency between self-reports and medical records for uterine fibroids in Taiwan cannot be checked. Fortunately, the literature support that self-reporting even years later remains a practical means for collecting data on menarche and uterine fibroids. We found that the impact of under-reporting of uterine fibroids is less in our study. In addition, the rate of reporting a diagnosis of uterine fibroids was within the rates of medical diagnosis based on national health insurance data. Future work investigating the consistency between self-reports and medical records in Taiwan can remedy this issue. WIDER IMPLICATIONS OF THE FINDINGS This study is the first to investigate whether and to what extent age at menarche mediates the causal effects of genetic variants on uterine fibroids by using genome-wide causal mediation analysis. By treating age at menarche as a mediator, this report provides an insight into the genetic risk factors for developing uterine fibroids. Thus, this article represents a step forward in deciphering the role of intermediated risk factors in the genetic mechanism of disease. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the China Medical University, Taiwan (CMU110-ASIA-13 and CMU107-Z-04), the Ministry of Science and Technology, Taiwan (MOST 110-2314-B-039-058) and the International Joint Usage/Research Center, the Institute of Medical Science, the University of Tokyo, Japan (K2104). The authors have no competing interests. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]