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

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

Racial Differences in Fatal Out-of-Hospital Coronary Heart Disease and the Role of Income in the Atherosclerosis Risk in Communities Cohort Study (1987 to 2017)

Black patients have higher incident fatal coronary heart disease (CHD) rates than do their White counterparts. Racial differences in out-of-hospital fatal CHD could explain the excess risk in fatal CHD among Black patients. We examined racial disparities in in- and out-of-hospital fatal CHD among participants with no history of CHD, and whether socioeconomic status might play a role in this association. We used data from the ARIC (Atherosclerosis Risk in Communities) study, including 4,095 Black and 10,884 White participants, followed between 1987 and 1989 until 2017. Race was self-reported. We examined racial differences in in- and out-of-hospital fatal CHD with hierarchical proportional hazard models. We then examined the role of income in these associations, using Cox marginal structural models for a mediation analysis. The incidence of out-of-hospital and in-hospital fatal CHD was 1.3 and 2.2 in Black participants, and 1.0 and 1.1 in White participants, respectively, per 1,000 person-years. The gender- and age-adjusted hazard ratios comparing out-of-hospital and in-hospital incident fatal CHD in Black with that in White participants were 1.65 (1.32 to 2.07) and 2.37 (1.96 to 2.86), respectively. The income-controlled direct effects of race in Black versus White participants decreased to 1.33 (1.01 to 1.74) for fatal out-of-hospital and to 2.03 (1.61 to 2.55) for fatal in-hospital CHD in Cox marginal structural models. In conclusion, higher rates of fatal in-hospital CHD in Black participants than in their White counterparts likely drive the overall racial differences in fatal CHD. Income largely explained racial differences in both fatal out-of-hospital CHD and fatal in-hospital CHD

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

A priori postulated and real power in cluster randomized trials: mind the gap

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screening or educational interventions. The intraclass correlation coefficient (ICC) defines the clustering effect and be specified during planning. The aim of this work is to study the influence of the ICC on power in cluster randomized trials. METHODS: Power contour graphs were drawn to illustrate the loss in power induced by an underestimation of the ICC when planning trials. We also derived the maximum achievable power given a specified ICC. RESULTS: The magnitude of the ICC can have a major impact on power, and with low numbers of clusters, 80% power may not be achievable. CONCLUSION: Underestimating the ICC during planning cluster randomized trials can lead to a seriously underpowered trial. Publication of a priori postulated and a posteriori estimated ICCs is necessary for a more objective reading: negative trial results may be the consequence of a loss of power due to a mis-specification of the ICC

Planning a cluster randomized trial with unequal cluster sizes: practical issues involving continuous outcomes

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screeening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes. METHODS: We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials. RESULTS: We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF) used in the sample size calculations has the best properties. CONCLUSION: Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (γ) of clusters actually recruit a proportion (τ) of subjects to be included (γ ≤ τ)"

High blood pressure in school children: prevalence and risk factors

BACKGROUND: The purpose of this study was to determine the prevalence of high blood pressure (HBP) and associated risk factors in school children 8 to 13 years of age. METHODS: Elementary school children (n = 1,066) were examined. Associations between HBP, body mass index (BMI), gender, ethnicity, and acanthosis nigricans (AN) were investigated using a school based cross-sectional study. Blood pressure was measured and the 95(th )percentile was used to determine HBP. Comparisons between children with and without HBP were utilized. The crude and multiple logistic regression adjusted odds ratios were used as measures of association. RESULTS: Females, Hispanics, overweight children, and children with AN had an increased likelihood of HBP. Overweight children (BMI ≥ 85(th )percentile) and those with AN were at least twice as likely to present with HBP after controlling for confounding factors. CONCLUSION: Twenty one percent of school children had HBP, especially the prevalence was higher among the overweight and Hispanic group. The association identified here can be used as independent markers for increased likelihood of HBP in children