Quantile regression for nonignorable missing data with its application of analyzing electronic medical records.

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

Analysis of secondary failure time responses in studies with response-dependent sampling schemes.

Response-dependent sampling is routinely used as an enrichment strategy in the design of family studies investigating the heritable nature of disease. In addition to the response of primary interest, investigators often wish to investigate the association between biomarkers and secondary responses related to possible comorbidities. Statistical analysis regarding genetic biomarkers and their association with the secondary outcome must address the biased sampling scheme involving the primary response. In this article, we develop composite likelihoods and two-stage estimation procedures for such secondary analyses in which the within-family dependence structure for the primary and secondary outcomes is modeled via a Gaussian copula. The dependence among responses within family members is modeled based on kinship coefficients. Auxiliary data from independent individuals are exploited by augmenting the composite likelihoods to increase precision of marginal parameter estimates and enhance the efficiency of estimators of the dependence parameters. Simulation studies are carried out to evaluate the finite sample performance of the proposed method, and an application to a motivating family study in psoriatic arthritis is given for illustration.

Highly Efficient Depolymerization of Waste Polyesters Enabled by Transesterification/Hydrogenation Relay Under Mild Conditions.

The efficient depolymerization of polyesters under mild conditions remains a significant challenge. Herein, we demonstrate a highly efficient strategy for the degradation of a diverse array of waste polyesters as low to 80 °C, 1 bar H2 . The key to the success of this transformation relied on the initial transesterification of macromolecular polyester into more degradable oligomeric fragments in the presence of CH3 OH and the subsequent hydrogenation by the use of the rationally designed quinaldine-based Ru complex. Controlled experiments and preliminary mechanistic studies disclosed the quinaldine-based catalysts could be hydrogenated to the eventually active species, which has been confirmed by X-ray diffraction analysis and directly used as a catalyst in the hydrogenolysis of polyester. The strong viability and high activity of this new species in protic solvent were explained in detail. Besides, the crucial role of CH3 OH in promoting reaction efficiency during the whole process was also elucidated. The synthetic utility of this method was further illustrated by preparing 1,4-cyclohexanedimethanol (CHDM) from waste polyethylene terephthalate (PET).

Essential regulators of iron chemical speciation distributions in anaerobic digestion of pretreated food waste: Organic volatile fatty acids or inorganic acid radicals?

In this work, a 30-days batched mesophilic assay on pretreated food waste (PFW) under different inoculum/substrate (I/S) ratios (1:5, 1:2, 1:1, 2:1, 4:1 and 1:0) was carried out, to target the most important parameters in AD matrix on regulating iron (Fe) chemical speciation. Correlation coefficients were calculated within four Fe chemical forms and AD parameters of pH, volatile fatty acids (VFAs), inorganic acid radicals (IARs), and alkalinity. Results showed that IARs were not key factors on regulating Fe speciation. Without acidification, IARs showed weak correlations (coefficients < 0.40) with Fe chemical dynamics while other parameters showed stronger correlations (coefficients ≥ 0.60). Under acidification, VFAs initiated the conversion of exchangeable Fe into water soluble fraction. Residual fraction might play important role in regulating Fe shifting to more bioavailable states.