Background: The dialysis patient population in the United States continues to grow. Trends in rates of death and hospitalization among dialysis patients have important consequences for outpatient dialysis capacity and Medicare spending. Objectives: To estimate contemporary trends in rates of death and hospitalization among dialysis patients in the United States, overall and within subgroups. Methods: We used Medicare Limited Data Sets (100% sample) in 2014–2017 to estimate trends in rates of death and hospitalization among dialysis patients with Medicare Parts A and B enrollment. We used seasonal autoregressive integrated moving average models to identify secular trends in the incidence of outcomes. Results: There were 631,075 unique patients; 222,924 deaths; and 1,876,779 hospital admissions. Weekly risks of both death and hospitalization exhibited strong seasonality. However, overall weekly risks of death were 34.9, 35.4, 35.2, and 35.7 deaths per 10,000 patients in 2014–2017, respectively (p = 0.47, from a likelihood ratio test of secular trend). The overall weekly risk of hospitalization was 3.08, 3.05, 3.11, and 3.11% in 2014, 2015, 2016, and 2017, respectively (p = 0.30). There were significant secular trends in risk of death in subgroups defined by black race and residency in South Atlantic states (p < 0.05). There were also secular trends in risk of hospitalization in subgroups defined by age 20–44 years, concurrent enrollment in Medicaid, and residency in South Central states. Conclusion: For the first time since the beginning of this century, rates of both death and hospitalization among dialysis patients with Medicare fee-for-service coverage have stagnated. The reasons for this change are unknown and require detailed assessment. Persistent lack of change in clinical outcomes may alter the future expectations about dialysis patient population growth.

Background: The dialysis patient population in the United States continues to grow. Trends in rates of death and hospitalization among dialysis patients have important consequences for outpatient dialysis capacity and Medicare spending. Objectives: To estimate contemporary trends in rates of death and hospitalization among dialysis patients in the United States, overall and within subgroups. Methods: We used Medicare Limited Data Sets (100% sample) in 2014–2017 to estimate trends in rates of death and hospitalization among dialysis patients with Medicare Parts A and B enrollment. We used seasonal autoregressive integrated moving average models to identify secular trends in the incidence of outcomes. Results: There were 631,075 unique patients; 222,924 deaths; and 1,876,779 hospital admissions. Weekly risks of both death and hospitalization exhibited strong seasonality. However, overall weekly risks of death were 34.9, 35.4, 35.2, and 35.7 deaths per 10,000 patients in 2014–2017, respectively (p = 0.47, from a likelihood ratio test of secular trend). The overall weekly risk of hospitalization was 3.08, 3.05, 3.11, and 3.11% in 2014, 2015, 2016, and 2017, respectively (p = 0.30). There were significant secular trends in risk of death in subgroups defined by black race and residency in South Atlantic states (p < 0.05). There were also secular trends in risk of hospitalization in subgroups defined by age 20–44 years, concurrent enrollment in Medicaid, and residency in South Central states. Conclusion: For the first time since the beginning of this century, rates of both death and hospitalization among dialysis patients with Medicare fee-for-service coverage have stagnated. The reasons for this change are unknown and require detailed assessment. Persistent lack of change in clinical outcomes may alter the future expectations about dialysis patient population growth.

Background: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. Methods: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. Results: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. Conclusion: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.

Background: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. Methods: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. Results: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. Conclusion: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.

Studies of complex human diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In GWAS, the principal components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals, and if family structure is present and ignored, such substructures may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao [Biometrika 1992;79:631-641], taking into account only siblings' relatedness, and by Oualkacha et al. [Stat Appl Genet Mol Biol 2012, DOI: 10.2202/1544-6115.1711], taking into account large pedigrees and high-dimensional phenotype data. In this work, we extend these methods to estimate the global individual ancestry coefficients from PCs derived from different variance component matrix estimators using SNPs from two simulated data sets and two real data sets: the GENOA sibship data consisting of European and African-American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from the Affymetrix 6.0 chip. Our results show that the family structure plays an important role in the estimation of the global individual ancestry value for extended pedigrees but not for sibships.

Studies of complex human diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In GWAS, the principal components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals, and if family structure is present and ignored, such substructures may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao [Biometrika 1992;79:631-641], taking into account only siblings' relatedness, and by Oualkacha et al. [Stat Appl Genet Mol Biol 2012, DOI: 10.2202/1544-6115.1711], taking into account large pedigrees and high-dimensional phenotype data. In this work, we extend these methods to estimate the global individual ancestry coefficients from PCs derived from different variance component matrix estimators using SNPs from two simulated data sets and two real data sets: the GENOA sibship data consisting of European and African-American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from the Affymetrix 6.0 chip. Our results show that the family structure plays an important role in the estimation of the global individual ancestry value for extended pedigrees but not for sibships.