- Our data suggest that PCA3 improves the diagnostic sensitivity and specificity of PSA and that the combination of PCA3 with PSA gives better overall performance in identification of PCa than serum PSA alone in the high-risk population.

Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where two types of events such as hospital admission and discharge occur alternately over time. The two alternating states defined by these recurrent events could each carry important and distinct information about a patient’s underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the two alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution. Moreover, the estimation procedure, which is based on smooth estimating equations, not only properly addresses challenges such as induced dependent censoring and intercept sampling bias commonly confronted in serial event gap time data, but also is more computationally tractable than the existing rank-based methods. The proposed methods are evaluated by simulation studies and illustrated by analyzing psychiatric contacts from the South Verona Psychiatric Case Register.

Summary In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this paper, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of length-biased sampling. To assess the covariate effects on the RMST, a semiparametric regression model that directly relates the covariates and the RMST is assumed. Based on the model, we develop unbiased estimating equations to obtain consistent estimators of covariate effects by properly adjusting for informative censoring and length bias. Stochastic process theories are used to establish the asymptotic properties of the proposed estimators. We investigate the finite sample performance through simulations and illustrate the methods by analyzing a prevalent cohort study of dementia in Canada.

Length-biased data are frequently encountered in prevalent cohort studies. Many statistical methods have been developed to estimate the covariate effects on the survival outcomes arising from such data while properly adjusting for length-biased sampling. Among them, regression methods based on the proportional hazards model have been widely adopted. However, little work has focused on checking the proportional hazards model assumptions with length-biased data, which is essential to ensure the validity of inference. In this article, we propose a statistical tool for testing the assumed functional form of covariates and the proportional hazards assumption graphically and analytically under the setting of length-biased sampling, through a general class of multiparameter stochastic processes. The finite sample performance is examined through simulation studies, and the proposed methods are illustrated with the data from a cohort study of dementia in Canada.

INTRODUCTION Patients with heart failure often have concomitant renal disease which can result in uremic platelet dysfunction. Determining whether uremia has affected platelets by platelet aggregometry can be challenging in these patients since they are often on antiplatelet medications. The current study was undertaken to determine if platelet aggregation studies could identify heart failure patients at risk for uremic bleeding prior to cardiac surgery. MATERIALS AND METHODS Platelet aggregation studies from three groups were studied and compared: 17 heart failure patients with mild to moderate renal impairment, 17 heart failure patient without renal abnormalities, and 17 healthy volunteers. RESULTS Platelet aggregation was severely impaired in both heart failure groups with and without renal abnormalities compared to healthy controls, and there were no significant differences in platelet aggregation in response to any of the agonists. There was a pan-decrease in platelet aggregation to all agonists in all heart failure patients. CONCLUSION Platelet aggregometry does not appear to be useful in measuring platelet dysfunction in heart failure patients with mild to moderate renal impairment.