Trivellore E Raghunathan

Harvard Medical School, Boston, Massachusetts, United States

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Publications (187)729.19 Total impact

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    ABSTRACT: Fat-soluble vitamin (FSV) deficiency is a well-recognized consequence of cholestatic liver disease and reduced intestinal intraluminal bile acid. We hypothesized that serum bile acid (SBA) would predict biochemical FSV deficiency better than serum total bilirubin (TB) level in infants with biliary atresia. Infants enrolled in the Trial of Corticosteroid Therapy in Infants with Biliary Atresia after hepatoportoenterostomy were the subjects of this investigation. Infants received standardized FSV supplementation and monitoring of TB, SBA, and vitamin levels at 1, 3, and 6 months. A logistic regression model was used with the binary indicator variable insufficient/sufficient as the outcome variable. Linear and nonparametric correlations were made between specific vitamin measurement levels and either TB or SBA. The degree of correlation for any particular vitamin at a specific time point was higher with TB than with SBA (higher for TB in 31 circumstances vs 3 circumstances for SBA). Receiver operating characteristic curve shows that TB performed better than SBA (area under the curve 0.998 vs 0.821). Including both TB and SBA did not perform better than TB alone (area under the curve 0.998). We found that TB was a better predictor of FSV deficiency than SBA in infants with biliary atresia. The role of SBA as a surrogate marker of FSV deficiency in other cholestatic liver diseases, such as progressive familial intrahepatic cholestasis, α-1-antitrypsin deficiency, and Alagille syndrome in which the pathophysiology is dominated by intrahepatic cholestasis, warrants further study.
    Journal of Pediatric Gastroenterology and Nutrition 12/2014; · 2.20 Impact Factor
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    ABSTRACT: To analyze the prevalence of acute asymptomatic group A and C rotavirus (RV-A and RV-C) infection in neonates with cholestasis. Participants were infants <180 days of age with cholestasis (serum direct or conjugated bilirubin >20% of total and ≥2 mg/dL) enrolled in the Childhood Liver Disease Research and Education Network during RV season (December-May). Forty infants with biliary atresia (BA), age 62 ± 29 days (range, 4.7-13 weeks) and 38 infants with cholestasis, age 67 ± 44 days (range, 3-15.8 weeks) were enrolled. At enrollment, RV-A IgM positivity rates did not differ between infants with BA (10%) vs those without (18%) (P = .349). RV-C IgM was positive in 0% of infants with BA vs 3% in those without BA (P = .49). RV-A IgG was lower in infants with BA: 51 ± 39 vs 56 ± 44 enzyme-linked immunoassay unit, P = .045 but this difference may lack biological relevance as maternal RV-A IgG titers were similar between groups. Infant RV-A IgM titers at 2-6 months follow-up increased markedly vs at presentation in both infants with BA (50 ± 30 vs 9 ± 9) and those without (43 ± 18 vs 16 ± 20 enzyme-linked immunoassay unit) (P < .0001), without differences between groups. RV-A infection in the first 6 months of life is common in infants with cholestasis of any cause. RV-A could have different pathogenetic effects by initiating different hepatic immune responses in infants with vs without BA or could lack pathogenetic significance. Copyright © 2014 Elsevier Inc. All rights reserved.
    The Journal of pediatrics. 10/2014;
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    ABSTRACT: Air pollution is linked to low lung function and respiratory events, yet little is known of associations with lung structure.
    Environmental Health Perspectives 10/2014; · 7.26 Impact Factor
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    ABSTRACT: Prior studies suggest that circulating n-3 and trans-fatty acids influence the risk of sudden cardiac arrest (SCA). Yet, while other fatty acids also differ in their membrane properties and biological activities which may influence SCA, little is known about the associations of other circulating fatty acids with SCA. The aim of this study was to investigate the associations of 17 erythrocyte membrane fatty acids with SCA risk. We used data from a population-based case-control study of SCA in the greater Seattle, Washington, area. Cases, aged 25–74 years, were out-of-hospital SCA patients, attended by paramedics (n=265). Controls, matched to cases by age, sex and calendar year, were randomly identified from the community (n=415). All participants were free of prior clinically-diagnosed heart disease. Blood was obtained at the time of cardiac arrest by attending paramedics (cases) or at the time of an interview (controls). Higher levels of erythrocyte very long-chain saturated fatty acids (VLSFA) were associated with lower risk of SCA. After adjustment for risk factors and levels of n-3 and trans-fatty acids, higher levels of 20:0 corresponding to 1 SD were associated with 30% lower SCA risk (13%-43%, p=0.001). Higher levels of 22:0 and 24:0 were associated with similar lower SCA risk (ORs for 1 SD-difference: 0.71 [95% CI: 0.57–0.88, p=0.002] for 22:0; and 0.79 [95% CI: 0.63–0.98, p=0.04] for 24:0). These novel findings support the need for investigation of biologic effects of circulating VLSFA and their determinants.
    Prostaglandins Leukotrienes and Essential Fatty Acids 10/2014; · 2.73 Impact Factor
  • Niko A. Kaciroti, Trivellore Raghunathan
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    ABSTRACT: Pattern-mixture models (PMM) and selection models (SM) are alternative approaches for statistical analysis when faced with incomplete data and a nonignorable missing-data mechanism. Both models make empirically unverifiable assumptions and need additional constraints to identify the parameters. Here, we first introduce intuitive parameterizations to identify PMM for different types of outcome with distribution in the exponential family; then we translate these to their equivalent SM approach. This provides a unified framework for performing sensitivity analysis under either setting. These new parameterizations are transparent, easy-to-use, and provide dual interpretation from both the PMM and SM perspectives. A Bayesian approach is used to perform sensitivity analysis, deriving inferences using informative prior distributions on the sensitivity parameters. These models can be fitted using software that implements Gibbs sampling. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 09/2014; · 2.04 Impact Factor
  • Joseph W. Sakshaug, Trivellore E. Raghunathan
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    ABSTRACT: Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.
    Journal of Applied Statistics 01/2014; 41(10). · 0.45 Impact Factor
  • Joseph W. Sakshaug, Trivellore Raghunathan
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    ABSTRACT: Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
    04/2013;
  • Brisa N Sánchez, Meihua Wu, Trivellore E Raghunathan, Ana V Diez-Roux
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    ABSTRACT: In many studies, it has been hypothesized that stress and its biologic consequences may contribute to disparities in rates of cardiovascular disease. However, understanding of the most appropriate statistical methods to analyze biologic markers of stress, such as salivary cortisol, remains limited. The authors explore the utility of various statistical methods in modeling daily cortisol profiles in population-based studies. They demonstrate that the proposed methods allow additional insight into the cortisol profile compared with commonly used summaries of the profiles based on raw data. For instance, one can gain insights regarding the shape of the population average curve, characterize the types of individual-level departures from the average curve, and better understand the relation between covariates and attained cortisol levels or slopes at various points of the day, in addition to drawing inferences regarding common features of the cortisol profile, such as the cortisol awakening response and the area under the curve. The authors compare the inference and interpretations drawn from these methods and use data collected as part of the Multi-Ethnic Study of Atherosclerosis to illustrate them.
    American journal of epidemiology 10/2012; · 5.59 Impact Factor
  • Yulei He, Trivellore E. Raghunathan
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    ABSTRACT: Multiple imputation has emerged as a popular approach to handling data sets with missing values. For incomplete continuous variables, imputations are usually produced using multivariate normal models. However, this approach might be problematic for variables with a strong non-normal shape, as it would generate imputations incoherent with actual distributions and thus lead to incorrect inferences. For non-normal data, we consider a multivariate extension of Tukey's gh distribution/transformation [38] to accommodate skewness and/or kurtosis and capture the correlation among the variables. We propose an algorithm to fit the incomplete data with the model and generate imputations. We apply the method to a national data set for hospital performance on several standard quality measures, which are highly skewed to the left and substantially correlated with each other. We use Monte Carlo studies to assess the performance of the proposed approach. We discuss possible generalizations and give some advices to practitioners on how to handle non-normal incomplete data.
    Journal of Applied Statistics. 10/2012; 39(10):2177-2198.
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    ABSTRACT: BACKGROUND: Accurate estimates of hypertension prevalence are critical for assessment of population health and for planning and implementing prevention and health care programs. While self-reported data is often more economically feasible and readily available compared to clinically measured HBP, these reports may underestimate clinical prevalence to varying degrees. Understanding the accuracy of self-reported data and developing prediction models that correct for underreporting of hypertension in self-reported data can be critical tools in the development of more accurate population level estimates, and in planning population-based interventions to reduce the risk of, or more effectively treat, hypertension. This study examines the accuracy of self-reported survey data in describing prevalence of clinically measured hypertension in two racially and ethnically diverse urban samples, and evaluates a mechanism to correct self-reported data in order to more accurately reflect clinical hypertension prevalence. METHODS: We analyze data from the Detroit Healthy Environments Partnership (HEP) Survey conducted in 2002 and the National Health and Nutrition Examination (NHANES) 2001--2002 restricted to urban areas and participants 25 years and older. We re-calibrate measures of agreement within the HEP sample drawing upon parameter estimates derived from the NHANES urban sample, and assess the quality of the adjustment proposed within the HEP sample RESULTS: Both self-reported and clinically assessed prevalence of hypertension were higher in the HEP sample (29.7 and 40.1, respectively) compared to the NHANES urban sample (25.7 and 33.8, respectively). In both urban samples, self-reported and clinically assessed prevalence is higher than that reported in the full NHANES sample in the same year (22.9 and 30.4, respectively). Sensitivity, specificity and accuracy between clinical and self-reported hypertension prevalence were 'moderate to good' within the HEP sample and 'good to excellent' within the NHANES sample. Agreement between clinical and self-reported hypertension prevalence was 'moderate to good' within the HEP sample (kappa =0.65; 95 % CI = 0.63-0.67), and 'good to excellent' within the NHANES sample (kappa =0.75; 95%CI = 0.73-0.80). Application of a 'correction' rule based on prediction models for clinical hypertension using the national sample (NHANES) allowed us to re-calibrate sensitivity and specificity estimates for the HEP sample. The adjusted estimates of hypertension in the HEP sample based on two different correction models, 38.1% and 40.5%, were much closer to the observed hypertension prevalence of 40.1 %. CONCLUSIONS: Application of a simple prediction model derived from national NHANES data to self-reported data from the HEP (Detroit based) sample resulted in estimates that more closely approximated clinically measured hypertension prevalence in this urban community. Similar correction models may be useful in obtaining more accurate estimates of hypertension prevalence in other studies that rely on self-reported hypertension.
    BMC Health Services Research 09/2012; 12(1):312. · 1.77 Impact Factor
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    ABSTRACT: Cholestasis predisposes to fat-soluble vitamin (FSV) deficiencies. A liquid multiple FSV preparation made with tocopheryl polyethylene glycol-1000 succinate (TPGS) is frequently used in infants with biliary atresia (BA) because of ease of administration and presumed efficacy. In this prospective multicenter study, we assessed the prevalence of FSV deficiency in infants with BA who received this FSV/TPGS preparation. Infants received FSV/TPGS coadministered with additional vitamin K as routine clinical care in a randomized double-blinded, placebo-controlled trial of corticosteroid therapy after hepatoportoenterostomy (HPE) for BA (identifier NCT 00294684). Levels of FSV, retinol binding protein, total serum lipids, and total bilirubin (TB) were measured 1, 3, and 6 months after HPE. Ninety-two infants with BA were enrolled in this study. Biochemical evidence of FSV insufficiency was common at all time points for vitamin A (29%-36% of patients), vitamin D (21%-37%), vitamin K (10%-22%), and vitamin E (16%-18%). Vitamin levels were inversely correlated with serum TB levels. Biochemical FSV insufficiency was much more common (15%-100% for the different vitamins) in infants whose TB was ≥2 mg/dL. At 3 and 6 months post HPE, only 3 of 24 and 0 of 23 infants, respectively, with TB >2 mg/dL were sufficient in all FSV. Biochemical FSV insufficiency is commonly observed in infants with BA and persistent cholestasis despite administration of a TPGS containing liquid multiple FSV preparation. Individual vitamin supplementation and careful monitoring are warranted in infants with BA, especially those with TB >2 mg/dL.
    PEDIATRICS 08/2012; 130(3):e607-14. · 4.47 Impact Factor
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    ABSTRACT: Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
    Statistics in Medicine 06/2012; · 2.04 Impact Factor
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    Niko A Kaciroti, Trivellore E Raghunathan, Jeremy M G Taylor, Stevo Julius
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    ABSTRACT: Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan-Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the inferences may not be valid. We propose an expanded PL method using a Bayesian framework to incorporate informative censoring mechanism and perform sensitivity analysis on estimates of the cumulative incidence curves. The expanded method uses a model, which can be viewed as a pattern mixture model, where odds for having an event during the follow-up interval $$({t}_{k-1},{t}_{k}]$$, conditional on being at risk at $${t}_{k-1}$$, differ across the patterns of missing data. The sensitivity parameters relate the odds of an event, between subjects from a missing-data pattern with the observed subjects for each interval. The large number of the sensitivity parameters is reduced by considering them as random and assumed to follow a log-normal distribution with prespecified mean and variance. Then we vary the mean and variance to explore sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the expanded model, thus allowing exploration of the sensitivity to inferences as departures from the inferences under the MAR assumption. The proposed approach is applied to data from the TRial Of Preventing HYpertension.
    Biostatistics 01/2012; 13(2):341-54. · 2.43 Impact Factor
  • Meihua Wu, Brisa N. Sanchez, Trivellore Raghunathan, Ana V. Diez-Roux
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    ABSTRACT: Defining the times at which samples are collected is an important aspect of longitudinal study design. Unfortunately, sampling times are often determined based on convenience and their optimality is seldom established statistically. Such convenient sampling strategy is likely to undermine the statistical efficiency of the study, especially in those cases where the response profile over time is nonlinear. Using the example of salivary cortisol, which exhibits a the nonlinear shape across the days, we discuss an approach for identifying efficient sampling protocols for studies where longitudinal measurements are collected. We model the response curves using conditionally linear mixed models, which allow some parameters to be nonlinear and accommodate flexible shapes of the longitudinal process. Our approach incorporates multi level random effects to account for between- subject and between-day variability commonly seen in epidemiological studies of the stress response. Furthermore, we propose a framework to include overall study costs when choosing the best sampling protocol.
    139st APHA Annual Meeting and Exposition 2011; 10/2011
  • Brisa N. Sanchez, Trivellore Raghunathan, Ana V. Diez-Roux, Meihua Wu
    [Show abstract] [Hide abstract]
    ABSTRACT: Many studies have hypothesized that stress and its biological consequences may contribute to disparities in cardiovascular disease. However, our understanding of the most appropriate statistical methods useful to analyze biological markers of stress such as salivary cortisol remains limited. We explore the utility of various statistical methods in modeling the stress response curve, as well as characterizing features of stress response in population based studies. In addition to commonly used summaries such as the cortisol awakening response and the area under the curve, we also propose alternative curve features. We compare the inference and interpretations drawn from these methods, and use data collected as part of the Multi-Ethnic Study of Artherosclerosis (MESA) to illustrate them.
    139st APHA Annual Meeting and Exposition 2011; 10/2011
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    Karen Farchaus Stein, Jeffrey Wing, Adam Lewis, Trivellore Raghunathan
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    ABSTRACT: This study sought to determine whether differential treatment effects in the targeted mechanisms of change and eating disorder (ED) symptoms are associated with patterns of attrition from a RCT. The main study was a RCT of a psychotherapy designed to alter the non-weight related self-cognitions as the means to promote recovery and health in a sample of 69 women with AN or BN. Four groups based on point of dropout were compared on demographic, self-cognitions and ED symptoms using logit and piecewise mixed effects modeling. Attrition was highest during treatment phase but no significant predictors were found. During the measurement phase, the direction and amount of change in self-cognition interrelatedness and body dissatisfaction differed according to point of dropout and treatment group. Attention to changes both in symptoms and mediating factors that occur during treatment and follow-up may help to identify those who are at risk for dropout and to develop strategies to promote RCT participant retention.
    International Journal of Eating Disorders 05/2011; 44(4):356-68. · 3.03 Impact Factor
  • Yulei He, Recai Yucel, Trivellore E Raghunathan
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    ABSTRACT: In designed longitudinal studies, information from the same set of subjects are collected repeatedly over time. The longitudinal measurements are often subject to missing data which impose an analytic challenge. We propose a functional multiple imputation approach modeling longitudinal response profiles as smooth curves of time under a functional mixed effects model. We develop a Gibbs sampling algorithm to draw model parameters and imputations for missing values, using a blocking technique for an increased computational efficiency. In an illustrative example, we apply a multiple imputation analysis to data from the Panel Study of Income Dynamics and the Child Development Supplement to investigate the gradient effect of family income on children's health status. Our simulation study demonstrates that this approach performs well under varying modeling assumptions on the time trajectory functions and missingness patterns.
    Statistics in Medicine 02/2011; 30(10):1137-56. · 2.04 Impact Factor
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    ABSTRACT: This study presents the results of an efficacy evaluation of a web-based brief motivational alcohol prevention/intervention program called Michigan Prevention and Alcohol Safety for Students (M-PASS). Four on-line sessions providing individually-tailored feedback were delivered to first-year college students over 9 weeks. Non- and low-risk drinking participants received risk prevention, while high-risk drinking participants received a risk-reduction intervention. Both intervention and control groups were surveyed at baseline and at a 3-month follow-up. Analysis showed positive effects for both men and women on stage of change, drinking behavior, drinking motivation and attitudes, and use of risk-reduction strategies. These results provided evidence of efficacy and found that M-PASS had both intervention and prevention effects, making it unique among currently developed brief alcohol interventions for college students.
    Journal of Drug Education 01/2011; 41(4):405-30. · 0.28 Impact Factor
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    ABSTRACT: Pathologists participating in the National Institutes of Health-sponsored Biliary Atresia Research Consortium (BARC) developed and then evaluated a standardized system for histologic reporting of liver biopsies from infants with cholestasis. A set of 97 anonymous liver biopsy samples was sent to 10 pathologists at BARC centers. A semiquantitative scoring system that had 16 histologic features was developed and then used by the pathologists, who had no knowledge of clinical history, imaging results, or laboratory data. Interobserver agreement was evaluated statistically. Agreement on scoring of each feature and on the pathologists' diagnosis, compared with the final clinical diagnosis, was evaluated by using weighted kappa statistics. There was moderate to substantial interobserver agreement in identification of bile plugs in ducts, giant-cell transformation, extramedullary hematopoiesis, and bile duct proliferation. The pathologists' diagnosis of obstruction in clinically proven cases of biliary atresia (BA) ranged from 79%-98%, with a positive predictive value of 90.7%. Histologic features that best predicted BA, on the basis of logistic regression, included bile duct proliferation, portal fibrosis, and absence of sinusoidal fibrosis (each P<.0001). The BARC histologic assessment system identified features of liver biopsies from cholestatic infants, with good interobserver agreement, that might be used in diagnosis and determination of prognosis. The system diagnosed BA with a high level of sensitivity and identified infants with biliary obstruction with reasonable interobserver agreement. However, distinguishing between BA and disorders such as total parenteral nutrition-associated liver disease and alpha(1)-antitrypsin deficiency is not possible without adequate clinical information.
    Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association 01/2011; 9(4):357-362.e2. · 5.64 Impact Factor

Publication Stats

5k Citations
729.19 Total Impact Points

Institutions

  • 2011–2014
    • Harvard Medical School
      • • Department of Medicine
      • • Department of Health Care Policy
      Boston, Massachusetts, United States
  • 1998–2014
    • University of Michigan
      • • Department of Biostatistics
      • • Department of Epidemiology
      • • Institute for Social Research
      Ann Arbor, Michigan, United States
    • Hebrew University of Jerusalem
      Yerushalayim, Jerusalem District, Israel
  • 2010
    • CUNY Graduate Center
      New York City, New York, United States
  • 2009–2010
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
  • 1991–2010
    • University of Washington Seattle
      • • Department of Medicine
      • • Department of Biostatistics
      Seattle, WA, United States
  • 2007–2008
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
    • Queensland University of Technology
      Brisbane, Queensland, Australia
    • Salt Lake City Community College
      Salt Lake City, Utah, United States
    • Centers for Disease Control and Prevention
      • National Center for Health Statistics
      Druid Hills, GA, United States
  • 2006–2008
    • Portland State University
      • School of Community Health
      Portland, OR, United States
  • 2005–2007
    • Morehouse School of Medicine
      • National Center for Primary Care
      Atlanta, GA, United States
    • Harvard University
      Cambridge, Massachusetts, United States
    • University of North Carolina at Chapel Hill
      • Department of Epidemiology
      Chapel Hill, NC, United States
  • 2004
    • Merck
      Whitehouse Station, New Jersey, United States
  • 2000
    • Virginia Commonwealth University
      • Department of Internal Medicine
      Richmond, VA, United States