Real-world effectiveness and safety of macitentan in patients with pulmonary artery hypertension: a multicenter, retrospective, observational study in China.

Background: Macitentan, either as monotherapy or part of combination therapy, improved clinical outcomes in patients with pulmonary artery hypertension (PAH) in clinical trials. Evidence on the effectiveness and safety of macitentan administered in real-world clinical practice in China is limited. Methods: This real-world, retrospective, multicenter chart review study was conducted at seven hospitals in China. Adult patients with a diagnosis of PAH who initiated macitentan and had medical assessments at 3-7 months after macitentan initiation were included. The primary outcomes were changes in the World Health Organization functional class (WHO-FC), 6-minute walk distance (6MWD), and N-terminal pro-B-type natriuretic peptide/B-type natriuretic peptide (NT-proBNP/BNP) from baseline to first follow-up visit (months 3-7). Serious adverse events (SAEs) and adverse drug reactions (ADRs) of macitentan were collected. Results: From 30 August 2021 to 31 March 2022, 214 eligible patients were included in the safety analysis set and 105 patients were included in the analysis of effectiveness. At the first follow-up visit compared with baseline, significant changes in WHO-FC were observed (p = .04), 93.5% patients had their WHO-FC improved (25.8%) or maintained (67.7%). 6MWD changed by a mean (standard deviation [SD]) of 45.0 (81.4) meters (p < .001), with 94.7% having their 6MWD improved (34.7%) or maintained (60.0%). The mean (SD) of NT-proBNP decreased from 1667.4 (3233.0) ng/L to 1090.0 (2230.1) ng/L (p < .001). In the safety analysis set, 24 (11.2%) patients experienced at least one ADR and/or SAE. ADRs and SAEs were reported in 11 (5.1%) and 18 (8.4%), respectively. No deaths or unexpected safety events were observed. Conclusion: This study provided real-world evidence on the clinical benefits and good tolerance of macitentan in Chinese patients with PAH treated in routine clinical practice.

FMEM: Functional Mixed Effects Models for Longitudinal Functional Responses.

The aim of this paper is to conduct a systematic and theoretical analysis of estimation and inference for a class of functional mixed effects models (FMEM). Such FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We propose local linear estimates of refined fixed effect functions and establish their weak convergence along with a simultaneous confidence band for each fixed-effect function. We propose a global test for the linear hypotheses of varying coefficient functions and derive the associated asymptotic distribution under the null hypothesis and the asymptotic power under the alternative hypothesis are derived. We also establish the convergence rates of the estimated spatial-temporal covariance operators and their associated eigenvalues and eigenfunctions. We conduct extensive simulations and apply our method to a white-matter fiber data set from a national database for autism research to examine the finite-sample performance of the proposed estimation and inference procedures.

Single-index varying coefficient model for functional responses.

Recently, massive functional data have been widely collected over space across a set of grid points in various imaging studies. It is interesting to correlate functional data with various clinical variables, such as age and gender, in order to address scientific questions of interest. The aim of this article is to develop a single-index varying coefficient (SIVC) model for establishing a varying association between functional responses (e.g., image) and a set of covariates. It enjoys several unique features of both varying-coefficient and single-index models. An estimation procedure is developed to estimate varying coefficient functions, the index function, and the covariance function of individual functions. The optimal integration of information across different grid points is systematically delineated and the asymptotic properties (e.g., consistency and convergence rate) of all estimators are examined. Simulation studies are conducted to assess the finite-sample performance of the proposed estimation procedure. Furthermore, our real data analysis of a white matter tract dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study confirms the advantage and accuracy of SIVC model over the popular varying coefficient model.

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data.

Motivated by studying large-scale longitudinal image data, we propose a novel functional nonlinear mixed effects modeling (FNMEM) framework to model the nonlinear spatial-temporal growth patterns of brain structure and function and their association with covariates of interest (e.g., time or diagnostic status). Our FNMEM explicitly quantifies a random nonlinear association map of individual trajectories. We develop an efficient estimation method to estimate the nonlinear growth function and the covariance operator of the spatial-temporal process. We propose a global test and a simultaneous confidence band for some specific growth patterns. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply FNMEM to investigate the spatial-temporal dynamics of white-matter fiber skeletons in a national database for autism research. Our FNMEM may provide a valuable tool for charting the developmental trajectories of various neuropsychiatric and neurodegenerative disorders.