Conditional modeling of panel count data with partly interval-censored failure event.

In longitudinal follow-up studies, panel count data arise from discrete observations on recurrent events. We investigate a more general situation where a partly interval-censored failure event is informative to recurrent events. The existing methods for the informative failure event are based on the latent variable model, which provides indirect interpretation for the effect of failure event. To solve this problem, we propose a failure-time-dependent proportional mean model with panel count data through an unspecified link function. For estimation of model parameters, we consider a conditional expectation of least squares function to overcome the challenges from partly interval-censoring, and develop a two-stage estimation procedure by treating the distribution function of the failure time as a functional nuisance parameter and using the B-spline functions to approximate unknown baseline mean and link functions. Furthermore, we derive the overall convergence rate of the proposed estimators and establish the asymptotic normality of finite-dimensional estimator and functionals of infinite-dimensional estimator. The proposed estimation procedure is evaluated by extensive simulation studies, in which the finite-sample performances coincide with the theoretical results. We further illustrate our method with a longitudinal healthy longevity study and draw some insightful conclusions.

Subgroup analysis in the heterogeneous Cox model.

In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox model by constructing a pairwise fusion penalized partial likelihood-based objective function. The proposed method can determine the number of subgroups, identify the group structure, and estimate the treatment effect simultaneously and automatically. A majorized alternating direction method of multipliers algorithm is then developed to deal with the numerically challenging high-dimensional problems. We also establish the oracle properties and the model selection consistency for the proposed penalized estimator. Our proposed method is evaluated by simulation studies and further illustrated by the analysis of the breast cancer data.

The anti-apoptosis effects of daidzein in the brain of D-galactose treated mice.

The purpose of this study was to explore the neuroprotective effects of daidzein on the apoptotic pathway in the hippocampus and cortex of D-galactose treated mice. For this purpose we have examined the expression of bcl-2 mRNA, bax mRNA and caspase-3 in the hippocampus and cortex of D-galactose-treated mice after fed with 10 or 5 mg/kg of daidzein. The results of in situ hybridization experiments indicate that daidzein could help increase the transcriptions of bcl-2 and decrease the transcriptions of bax in those brain regions of D-galactose-treated mice. Furthermore, immunohistochemical studies showed that daidzein could reduce the expression of caspase-3 in both brain regions. These results suggest that daidzein in soybean can inhibit the D-gal induced apoptosis via Bcl-2/Bax apoptotic pathway and be a potential medical candidate for neurodegeneration therapy.