Post-Selection Inference for the Cox Model with Interval-Censored Data

We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence intervals conditional on the model selected by lasso. The method is based on a pivotal quantity that is shown to converge to a uniform distribution under local alternatives. The proof can be adapted to many other regression models, which is illustrated by the extension to generalized linear models and the Cox model with right-censored data. Our method involves estimation of the efficient information matrix, for which several approaches are proposed with proof of their consistency. Thorough simulation studies show that our method has satisfactory performance in samples of modest sizes. The utility of the method is illustrated via an application to an Alzheimer's disease study.Comment: 46 pages, 14 figure

Supramolecular compartmentalized hydrogels via polydopamine particle-stabilized water-in-water emulsions

Compartmentalized hydrogels constitute a significant research area, for example, for catalytic and biomedical applications. As presented here, a generic method is used for compartmentalization of supramolecular hydrogels by using water-in-water emulsions based on aqueous two-phase systems. By forming the supramolecular hydrogel throughout the continuous phase of all-aqueous emulsions, distinct, microcompartmentalized materials were created. The basis for the presented compartmentalized water-in-water hydrogels is polydopamine particle-stabilized water-in-water emulsions from dextran and poly(ethylene glycol) (PEG). Addition of α-cyclodextrin (α-CD) led to supramolecular complexation with PEG and subsequent hydrogel formation showing no signs of creaming. Due to the supramolecular nature of the compartmentalized hydrogels, selective network cleavage could be induced via competing guest addition, while keeping the emulsion substructure intact