Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease with a frequency as high as 1 in 200. In many cases, HCM is caused by mutations in genes encoding the different components of the sarcomere apparatus. HCM is characterized by unexplained left ventricular hypertrophy (LVH), myofibrillar disarray, and myocardial fibrosis. The phenotypic expression is quite variable. While the majority of patients with HCM are asymptomatic, serious consequences are experienced in a subset of affected individuals who present initially with sudden cardiac death (SCD) or progress to refractory heart failure (HF). The HCMR study is a National Heart Lung and Blood Institute (NHLBI)-sponsored 2750 patient, 41 site, international registry and natural history study designed to address limitations in extant evidence to improve prognostication in HCM (NCT01915615). In addition to collection of standard demographic, clinical, and echocardiographic variables, patients will undergo state-of-the-art cardiac magnetic resonance (CMR) for assessment of left ventricular (LV) mass and volumes as well as replacement scarring and interstitial fibrosis. In addition, genetic and biomarker analysis will be performed. HCMR has the potential to change the paradigm of risk stratification in HCM, using novel markers to identify those at higher risk.

The nonparametric Mann-Whitney-Wilcoxon (MWW) rank sum test is widely used to test treatment effect by comparing the outcome distributions between two groups, especially when there are outliers in the data. However, such statistics generally yield invalid conclusions when applied to nonrandomized studies, particularly those in epidemiologic research. Although one may control for selection bias by using available approaches of covariates adjustment such as matching, regression analysis, propensity score matching, and marginal structural models, such analyses yield results that are not only subjective based on how the outliers are handled but also often difficult to interpret. A popular alternative is a conditional permutation test based on randomization inference [Rosenbaum PR. Covariance adjustment in randomized experiments and observational studies. Statistical Science 2002; 17(3):286-327]. Because it requires strong and implausible assumptions that may not be met in most applications, this approach has limited applications in practice. In this paper, we address this gap in the literature by extending MWW and other nonparametric statistics to provide causal inference for nonrandomized study data by integrating the potential outcome paradigm with the functional response models (FRM). FRM is uniquely positioned to model dynamic relationships between subjects, rather than attributes of a single subject as in most regression models, such as the MWW test within our context. The proposed approach is illustrated with data from both real and simulated studies.

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Hypertensive disorders of pregnancy (HDP) are associated with cardiovascular disease (CVD) later in life. We investigated the association of HDP with blood pressure (BP) and arterial stiffness 1-year postpartum. Seventy-four participants, 33 with an HDP and 41 with uncomplicated pregnancies, were examined using applanation tonometry to measure BP, carotid-femoral pulse-wave velocity (cfPWV) and augmentation index (AIx). On average, women with HDP had a 9 mm higher systolic BP (p<.01), 0.8 m/s faster cfPWV (p=.09), and 5.4% greater AIx (p=.09) at the 1-year exam. After adjustment for covariates, there was no significant difference in cfPWV between groups, while a 7.3% greater AIx (p<.05) remained. These findings suggest reduced endothelial function may be detected 1 year after HDP. Large prospective studies are needed to further understand of the contribution of arterial stiffness and endothelial dysfunction to the evolution of CVD disease after these complicated pregnancies.

Objectives To examine the relationship between infant feeding and risk of child overweight and obesity across race and ethnicity in a diverse community-based cohort. Methods 2172 mother baby dyads were drawn from a prospective cohort constructed using data from electronic medical records linked to birth records. The primary exposure was exclusive breastfeeding at 2 months of age; outcome was BMI Z-score and BMI ≥ 85th percentile (overweight and obese) at 4 years of age. Regression models were adjusted for confounding using covariance balanced propensity score and inverse probability weighting. Results At age 4, exclusively breast fed children had lower BMI Z-score (-0.109, SE = 0.048) and a decreased odds of a BMI ≥ 85th percentile (0.832; 95 % CI 0.792, 0.994), when compared to those exclusively formula-fed or had mixed feeding. Race and ethnicity significantly moderated these associations. Sub-population analysis showed the effect was significant for BMI Z-score (p = 0.0002) and BMI ≥ 85th percentile (p < 0.0001) only for children of NH white mothers. For children of NH black mothers exclusive breastfeeding was not associated with a significant difference in BMI Z-score, however there was an increased odds of overweight or obesity (p = 0.0145). Conclusions The protective effect of breastfeeding against early childhood overweight and obesity may differ by race and ethnicity. This suggests that programs aiming to reduce obesity by increasing rates of breastfeeding may have limited impact for some groups and should be coupled with other racially and ethnically focused efforts to encourage healthy feeding practices in infancy and early childhood.

Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated. In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.

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