High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study.

BACKGROUND: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach.

BACKGROUND: Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. METHOD: We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. RESULTS: Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. CONCLUSIONS: Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

Healthy life expectancy for 202 countries up to 2030: Projections with a Bayesian model ensemble.

Background: Healthy life expectancy (HLE) projections are required for optimising social and health service management in the future. Existing studies on the topic were usually conducted by selecting a single model for analysis. We thus aimed to use an ensembled model to project the future HLE for 202 countries/region. Methods: We obtained data on age-sex-specific HLE and the sociodemographic index (SDI) level of 202 countries from 1990 to 2019 from the Global Burden of Disease (GBD) database and used a probabilistic Bayesian model comprised of 21 forecasting models to predict their HLE in 2030. Results: In general, HLE is projected to increase in all 202 countries, with the least probability of 82.4% for women and 81.0% for men. Most of the countries with the lowest projected HLE would be located in Africa. Women in Singapore have the highest projected HLE in 2030, with a 94.5% probability of higher than 75.2 years, which is the highest HLE in 2019 across countries. Maldives, Kuwait, and China are projected to have a probability of 49.3%, 41.2% and 31.6% to be the new entries of the top ten countries with the highest HLE for females compared with 2019. Men in Singapore are projected to have the highest HLE at birth in 2030, with a 93.4% probability of higher than 75.2 years. Peru and Maldives have a probability of 48.7% and 35.3% being new top ten countries in male's HLE. The female advantage in HLE will shrink by 2030 in 117 countries, especially in most of the high SDI and European countries. Conclusions: HLE will likely continue to increase in most countries and regions worldwide in the future. More attention needs to be paid to combatting obesity, chronic diseases, and specific infectious diseases, especially in African and some Pacific Island countries. Although gender gaps may not be fully bridged, HLE could partially mitigate and even eliminate them through economic development and improvements in health care.