In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment effect in the treated (ATT). The variance of the IPW ATE estimator is often estimated by assuming that the weights are known and then using the so-called "robust" (Huber-White) sandwich estimator, which results in conservative standard errors (SEs). Here we show that using such an approach when estimating the variance of the IPW ATT estimator does not necessarily result in conservative SE estimates. That is, assuming the weights are known, the robust sandwich estimator may be either conservative or anticonservative. Thus, confidence intervals for the ATT using the robust SE estimate will not be valid, in general. Instead, stacked estimating equations which account for the weight estimation can be used to compute a consistent, closed-form variance estimator for the IPW ATT estimator. The 2 variance estimators are compared via simulation studies and in a data analysis of the association between smoking and gene expression.
Background: The most prevalent endocrine disorder affecting women is PCOS. Programmed death of ovarian cells has yet to be elucidated. Ferroptosis is a kind of iron-dependent necrosis featured by significantly Fe-dependent lipid peroxidation.
Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, this can pose a challenge to learning the effect of treatment, since not all participants have outcome assessments at the times of interest. Furthermore, observed outcome values may not be representative of all participants' outcomes at a given time.
Phasing, the process of determining which alleles at different loci on homologous chromosomes belong together on the same chromosome, is crucial in the diagnosis and management of autosomal recessive diseases. Advances in long-read sequencing technologies have significantly enhanced our ability to accurately determine haplotypes. This review discusses the application of low-coverage long-read sequencing, nanopore Cas9-guided long-read sequencing, and adaptive sampling in phasing, highlighting their utility in complex clinical scenarios.
The tyrosine kinase 2 inhibitor deucravacitinib is therapeutically effective for psoriasis. However, predictive factors for high responses to deucravacitinib have not been examined in a real-world clinical study. Our study aimed to identify predictive factors for responders to deucravacitinib.
Objectives: To explore the effect and the probable mechanisms of JLD in the treatment of type 2 diabetes mellitus (T2DM) - associated cognitive impairment (TDACI).
Methods: The effect of JLD in combating TDACI was assessed in T2DM model mice by conducting Morris water maze (MWM) behaviour testing. Active components and their putative targets, as well as TDACI-related targets, were collected from public databases.
