Faculty Bibliography

BACKGROUND: The surgical robot has been widely adopted in the United States in spite of its high cost and controversy surrounding its benefit. Some have suggested that a "medical arms race" influences technology adoption. We wanted to determine whether a hospital would acquire a surgical robot if its nearest neighboring hospital already owned one. METHODS: We identified 554 hospitals performing radical prostatectomy from the Healthcare Cost and Utilization Project Statewide Inpatient Databases for seven states. We used publicly available data from the website of the surgical robot's sole manufacturer (Intuitive Surgical, Sunnyvale, CA) combined with data collected from the hospitals to ascertain the timing of robot acquisition during year 2001 to 2008. One hundred thirty four hospitals (24%) had acquired a surgical robot by the end of 2008. We geocoded the address of each hospital and determined a hospital's likelihood to acquire a surgical robot based on whether its nearest neighbor owned a surgical robot. We developed a Markov chain method to model the acquisition process spatially and temporally and quantified the "neighborhood effect" on the acquisition of the surgical robot while adjusting simultaneously for known confounders. RESULTS: After adjusting for hospital teaching status, surgical volume, urban status and number of hospital beds, the Markov chain analysis demonstrated that a hospital whose nearest neighbor had acquired a surgical robot had a higher likelihood itself acquiring a surgical robot. (OR=1.71, 95% CI: 1.07-2.72, p=0.02). CONCLUSION: There is a significant spatial and temporal association for hospitals acquiring surgical robots during the study period. Hospitals were more likely to acquire a surgical robot during the robot's early adoption phase if their nearest neighbor had already done so.

Nested case-control sampling is a popular design for large epidemiological cohort studies due to its cost effectiveness. A number of methods have been developed for the estimation of the proportional hazards model with nested case-control data; however, the evaluation of modeling assumption is less attended. In this article, we propose a class of goodness-of-fit test statistics for testing the proportional hazards assumption based on nested case-control data. The test statistics are constructed based on asymptotically mean-zero processes derived from Samuelsen's maximum pseudo-likelihood estimation method. In addition, we develop an innovative resampling scheme to approximate the asymptotic distribution of the test statistics while accounting for the dependent sampling scheme of nested case-control design. Numerical studies are conducted to evaluate the performance of our proposed approach, and an application to the Wilms' Tumor Study is given to illustrate the methodology.

Epidemiologic studies that evaluated genetic susceptibility to the effects of arsenic exposure from drinking water on subclinical atherosclerosis are limited. We conducted a cross-sectional study of 1,078 participants randomly selected from the Health Effects of Arsenic Longitudinal Study in Bangladesh to evaluate whether the association between arsenic exposure and carotid artery intima-medial thickness (cIMT) differs by 207 single-nucleotide polymorphisms (SNPs) in 18 genes related to arsenic metabolism, oxidative stress, inflammation, and endothelial dysfunction. Although not statistically significant after correcting for multiple testing, nine SNPs in APOE, AS3MT, PNP, and TNF genes had a nominally statistically significant interaction with well-water arsenic in cIMT. For instance, the joint presence of a higher level of well-water arsenic (>/= 40.4 mug/L) and the GG genotype of AS3MT rs3740392 was associated with a difference of 40.9 mum (95% CI=14.4, 67.5) in cIMT, much greater than the difference of cIMT associated with the genotype alone (beta=-5.1 mum, 95% CI=-31.6, 21.3) or arsenic exposure alone (beta=7.2 mum, 95% CI=-3.1, 17.5). The pattern and magnitude of the interactions were similar when urinary arsenic was used as the exposure variable. Additionally, the at-risk genotypes of the AS3MT SNPs were positively related to proportion of monomethylarsonic acid (MMA) in urine, which is indicative of arsenic methylation capacity. The findings provide novel evidence that genetic variants related to arsenic metabolism may play an important role in arsenic-induced subclinical atherosclerosis. Future replication studies in diverse populations are needed to confirm the findings.

The nested case-control (NCC) design is widely used in epidemiologic studies as a cost-effective subcohort sampling method to study the association between a disease and its potential risk factors. NCC data are commonly analyzed using Thomas' partial likelihood approach under the Cox proportional hazards model assumption. However, the linear modeling form in the Cox model may be insufficient for practical applications, especially when there are a large number of risk factors under investigation. In this paper, we consider a partially linear single index proportional hazard model, which includes a linear component for covariates of interest to yield easily interpretable results and a nonparametric single index component to adjust for multiple confounders effectively. We propose to approximate the nonparametric single index function by polynomial splines and estimate the parameters of interest using an iterative algorithm based on the partial likelihood. Asymptotic properties of the resulting estimators are established. The proposed methods are evaluated using simulations and applied to an NCC study of ovarian cancer.

Bioactive nanoscale arrays were constructed to ligate activating cell surface receptors on T cells (the CD3 component of the TCR complex) and natural killer (NK) cells (CD16). These arrays are formed from biofunctionalized gold nanospheres with controlled interparticle spacing in the range 25-104 nm. Responses to these nanoarrays were assessed using the extent of membrane-localized phosphotyrosine in T cells stimulated with CD3-binding nanoarrays and the size of cell contact area for NK cells stimulated with CD16-binding nanoarrays. In both cases, the strength of response decreased with increasing spacing, falling to background levels by 69 nm in the T cell/anti-CD3 system and 104 nm for the NK cell/anti-CD16 system. These results demonstrate that immune receptor triggering can be influenced by the nanoscale spatial organization of receptor/ligand interactions.

Objectives:To assess associations of caesarean section with body mass from birth through adolescence.Design:Longitudinal birth cohort study, following subjects up to 15 years of age.Setting and participants:Children born in 1991-1992 in Avon, UK who participated in the Avon Longitudinal Study of Parents and Children (ALSPAC) (n=10 219).Outcome measures:Primary outcome: standardized measures of body mass (weight-for length z-scores at 6 weeks, 10 and 20 months; and body mass index (BMI) z-scores at 38 months, 7, 9, 11 and 15 years). Secondary outcome: categorical overweight or obese (BMI >/=85th percentile) for age and gender, at 38 months, 7, 9, 11 and 15 years.Results:Of the 10 219 children, 926 (9.06%) were delivered by caesarean section. Those born by caesarean had lower-birth weights than those born vaginally (-46.1 g, 95% confidence interval(CI): 14.6-77.6 g; P=0.004). In mixed multivariable models adjusting for birth weight, gender, parental body mass, family sociodemographics, gestational factors and infant feeding patterns, caesarean delivery was consistently associated with increased adiposity, starting at 6 weeks (+0.11 s.d. units, 95% CI: 0.03-0.18; P=0.005), through age 15 (BMI z-score increment+0.10 s.d. units, 95% CI: 0.001-0.198; P=0.042). By age 11 caesarean-delivered children had 1.83 times the odds of overweight or obesity (95% CI: 1.24-2.70; P=0.002). When the sample was stratified by maternal pre-pregnancy weight, the association among children born of overweight/obese mothers was strong and long-lasting. In contrast, evidence of an association among children born of normal-weight mothers was weak.Conclusion:Caesarean delivery is associated with increased body mass in childhood and adolescence. Research is needed to further characterize the association in children of normal weight women. Additional work is also needed to understand the mechanism underlying the association, which may involve relatively enduring changes in the intestinal microbiome.International Journal of Obesity advance online publication, 14 May 2013; doi:10.1038/ijo.2013.49.

We conducted a cross-sectional study to evaluate the interrelationships between past arsenic exposure, biomarkers specific for susceptibility to arsenic exposure, and carotid intima-media thickness (cIMT) in 959 subjects from the Health Effects of Arsenic Longitudinal Study in Bangladesh. We measured cIMT levels on average 7.2 years after baseline during 2010-2011. Arsenic exposure was measured in well water at baseline and in urine samples collected at baseline and during follow-up. Every 1-standard-deviation increase in urinary arsenic (357.9 microg/g creatinine) and well-water arsenic (102.0 microg/L) concentration was related to a 11.7-microm (95% confidence interval (CI): 1.8, 21.6) and 5.1-microm (95% CI: -0.2, 10.3) increase in cIMT, respectively. For every 10% increase in monomethylarsonic acid (MMA) percentage, there was an increase of 12.1 microm (95% CI: 0.4, 23.8) in cIMT. Among participants with a higher urinary MMA percentage, a higher ratio of urinary MMA to inorganic arsenic, and a lower ratio of dimethylarsinic acid to MMA, the association between well-water arsenic and cIMT was stronger. The findings indicate an effect of past long-term arsenic exposure on cIMT, which may be potentiated by suboptimal or incomplete arsenic methylation capacity. Future prospective studies are needed to confirm the association between arsenic methylation capacity and atherosclerosis-related outcomes.

BACKGROUND: Prospective studies that evaluate the influence of arsenic methylation capacity on cardiovascular disease (CVD) risk are lacking. OBJECTIVE: To evaluate the association of arsenic exposure from drinking water and arsenic methylation capacity with CVD risk. METHOD: We conducted a case-cohort study of 369 incident fatal and non-fatal cases of CVD, including 148 stroke cases and 211 cases of heart disease, and a subcohort of 1,109 subjects randomly selected from the 11,224 participants in the Health Effects of Arsenic Longitudinal Study. RESULTS: The adjusted hazard ratio (HR) for all CVD, heart disease, and stroke in association with a standard deviation increase in baseline well arsenic (112 microg/L) was 1.15 (95% CI: 1.01, 1.30), 1.20 (95% CI: 1.04, 1.38), and 1.08 (95% CI: 0.90, 1.30), respectively. Adjusted HRs for the second and third tertiles of urinary monomethylarsonic acid (MMA)% relative to the lowest tertile, respectively, were 1.27 (95% CI: 0.85, 1.90) and 1.55 (95% CI: 1.08, 2.23) for all CVD, and 1.65 (95% CI: 1.05, 2.60) and 1.61 (95% CI: 1.04, 2.49) for heart disease specifically. The highest versus lowest ratio of urinary dimethylarsinic acid (DMA) to MMA was associated with a significantly decreased risk of CVD (HR=0.54; 95% CI: 0.34, 0.85) and heart disease (HR=0.54; 95% CI: 0.33, 0.88). There was no apparent association between arsenic metabolite indices and stroke risk. The joint effects of incomplete arsenic methylation capacity, indicated by higher urinary MMA% or lower urinary DMA%, with higher levels of well arsenic on heart disease risk were additive. There was some evidence of a synergy of incomplete methylation capacity with older age and cigarette smoking. CONCLUSIONS: Arsenic exposure from drinking water and incomplete methylation capacity of arsenic were adversely associated with heart disease risk.

A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this, researchers often join forces by bringing multiple studies together to achieve large sample sizes, allowing for increased power in hypothesis testing, and improved efficiency in effect estimation. Combining studies, however, renders the analysis difficult owing to the presence of heterogeneity in the pooled data. In this article, motivated by a collaborative nested case-control (NCC) study of ovarian cancer in three cohorts from United States, Sweden, and Italy, we investigate the use of penalty regularized partial likelihood estimation in the context of pooled NCC studies to achieve two goals. First, we propose an adaptive group lasso (gLASSO) penalized approach to simultaneously identify important variables and estimate their effects. Second, we propose a composite agLASSO penalized approach to identify variables with heterogeneous effects. Both methods are readily implemented with the group coordinate gradient decent algorithm and shown to enjoy the oracle property. We conduct simulation studies to evaluate the performance of our proposed approaches in finite samples under various heterogeneity settings, and apply them to the pooled ovarian cancer study.