Continuous-time causal mediation analysis

A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.

Effect of Carnitine Supplementation in Pediatric Patients with Left Ventricular Dysfunction

Carnitine is an essential amino acid involved in transporting fatty acids across the mitochondrial membrane. Fatty acids are a primary source of energy for the myocardium. Studies in adults demonstrated decreased carnitine levels in the ischemic myocardium, but subsequent exogenous carnitine supplementation showed improvement of myocardial metabolism and left ventricular function. However, only limited data regarding carnitine are available in pediatrics. A single-center retrospective, paired data study was conducted. Patients < 18 years, left ventricular ejection fraction (LVEF) < 55% by echocardiography, and had received at least 7 days of oral or intravenous carnitine supplementation between January 2018 and March 2021 are included in the study. Several endpoints and covariates were collected for each patient: before, one week after, one month after, and 6 months after carnitine supplementation. Univariate analysis consisted of an analysis of variance (ANOVA), followed by an analysis of covariance (ANCOVA) to model LVEF while adjusting for other variables. 44 patients included in the final analyses. LVEF significantly improved from 50.5 to 56.6% (p < 0.01). When LVEF was adjusted for other interventions (mechanical ventilation, afterload reduction, diuretic therapy, spironolactone), the estimated means demonstrated a significant increase from 45.7 to 58.0% (p < 0.01). Free carnitine level increased significantly (p = 0.03), and N-terminal-pro-brain natriuretic peptide (p = 0.03), creatinine (p < 0.01), and lactate (p < 0.01) all significantly decreased over the study period. Carnitine supplementation in pediatric patients with left ventricular systolic dysfunction may be associated with an increase in LVEF and improvement in laboratory markers of myocardial stress and cardiac output.

Methods for Modeling Autocorrelation and Handling Missing Data in Mediation Analysis in Single Case Experimental Designs (SCEDs)

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.

Adiposity and Endometrial Cancer Risk in Postmenopausal Women: A Sequential Causal Mediation Analysis

BACKGROUND

Adiposity increases endometrial cancer risk, possibly through inflammation, hyperinsulinemia, and increasing estrogens. We aimed to quantify the mediating effects of adiponectin (anti-inflammatory adipocytokine); IL6, IL1-receptor antagonist, TNF receptor 1 and 2, and C-reactive protein (inflammatory status biomarkers); C-peptide (hyperinsulinemia biomarker); and free estradiol and estrone (estrogen biomarkers) in the adiposity-endometrial cancer link in postmenopausal women.

METHODS

We used data from a case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC). Eligible women did not have cancer, hysterectomy, and diabetes; did not use oral contraceptives or hormone therapy; and were postmenopausal at recruitment. Mediating pathways from adiposity to endometrial cancer were investigated by estimating natural indirect (NIE) and direct (NDE) effects using sequential mediation analysis.

RESULTS

The study included 163 cases and 306 controls. The adjusted OR for endometrial cancer for body mass index (BMI) ≥30 versus ≥18.5-<25 kg/m2 was 2.51 (95% confidence interval, 1.26-5.02). The ORsNIE were 1.95 (1.01-3.74) through all biomarkers [72% proportion mediated (PM)] decomposed as: 1.35 (1.06-1.73) through pathways originating with adiponectin (33% PM); 1.13 (0.71-1.80) through inflammation beyond (the potential influence of) adiponectin (13% PM); 1.05 (0.88-1.24) through C-peptide beyond adiponectin and inflammation (5% PM); and 1.22 (0.89-1.67) through estrogens beyond preceding biomarkers (21% PM). The ORNDE not through biomarkers was 1.29 (0.54-3.09). Waist circumference gave similar results.

CONCLUSIONS

Reduced adiponectin and increased inflammatory biomarkers, C-peptide, and estrogens mediated approximately 70% of increased odds of endometrial cancer in women with obesity versus normal weight.

IMPACT

If replicated, these results could have implications for identifying targets for intervention to reduce endometrial cancer risk in women with obesity.