Covariate-Adjusted Constrained Bayes Predictions of Random Intercepts and Slopes. Sujit Ghosh is a

Constrained Bayes methodology represents an alternative to the posterior mean (empirical Bayes) method commonly used to produce random effect predictions under mixed linear models. The general constrained Bayes methodology of Ghosh (1992) is compared to a direct implementation of constraints, and it is suggested that the former approach could feasibly be incorporated into commercial mixed model software. Simulation studies and a real-data example illustrate the main points and support the conclusions

Differences in incident and recurrent myocardial infarction among White and Black individuals aged 35 to 84: Findings from the ARIC community surveillance study

Background: No previous study has examined racial differences in recurrent acute myocardial infarction (AMI) in a community population. We aimed to examine racial differences in recurrent AMI risk, along with first AMI risk in a community population. Methods: The community surveillance of the Atherosclerosis Risk in Communities Study (2005-2014) included 470,000 people 35 to 84 years old in 4 U.S. communities. Hospitalizations for recurrent and first AMI were identified from ICD-9-CM discharge codes. Poisson regression models were used to compare recurrent and first AMI risk ratios between Black and White residents. Results: Recurrent and first AMI risk per 1,000 persons were 8.8 (95% CI, 8.3-9.2) and 20.7 (95% CI, 20.0-21.4) in Black men, 6.8 (95% CI, 6.5-7.0) and 14.1 (95% CI, 13.8-14.5) in White men, 5.3 (95% CI, 5.0-5.7) and 16.2 (95% CI, 15.6-16.8) in Black women, and 3.1 (95% CI, 3.0-3.3) and 8.8 (95% CI, 8.6-9.0) in White women, respectively. The age-adjusted risk ratios (RR) of recurrent AMI were higher in Black men vs White men (RR, 1.58 95% CI, 1.30-1.92) and Black women vs White women (RR, 2.09 95% CI, 1.64-2.66). The corresponding RRs were slightly lower for first AMI: Black men vs White men, RR, 1.49 (95% CI, 1.30-1.71) and Black women vs White women, RR, 1.65 (95% CI, 1.42-1.92) Conclusions: Large disparities exist by race for recurrent AMI risk in the community. The magnitude of disparities is stronger for recurrent events than for first events, and particularly among women

Modelling stochastic bivariate mortality

Stochastic mortality, i.e. modelling death arrival via a jump process with stochastic intensity, is gaining increasing reputation as a way to represent mortality risk. This paper represents a first attempt to model the mortality risk of couples of individuals, according to the stochastic intensity approach. On the theoretical side, we extend to couples the Cox processes set up, i.e. the idea that mortality is driven by a jump process whose intensity is itself a stochastic process, proper of a particular generation within each gender. Dependence between the survival times of the members of a couple is captured by an Archimedean copula. On the calibration side, we fit the joint survival function by calibrating separately the (analytical) copula and the (analytical) margins. First, we select the best fit copula according to the methodology of Wang and Wells (2000) for censored data. Then, we provide a sample-based calibration for the intensity, using a time-homogeneous, non mean-reverting, affine process: this gives the analytical marginal survival functions. Coupling the best fit copula with the calibrated margins we obtain, on a sample generation, a joint survival function which incorporates the stochastic nature of mortality improvements and is far from representing independency.On the contrary, since the best fit copula turns out to be a Nelsen one, dependency is increasing with age and long-term dependence exists