[Show abstract][Hide abstract] 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; 59(6). DOI:10.1097/MPG.0000000000000547 · 2.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background: Air pollution is linked to low lung function and to respiratory events, yet little is known of associations with lung structure.
Objectives: We examined associations of particulate matter (PM2.5, PM10) and nitrogen oxides (NOx) with percent emphysema-like lung on computed tomography (CT).
Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) recruited participants (45–84 years of age) in six U.S. states. Percent emphysema was defined as lung regions < –910 Hounsfield Units on cardiac CT scans acquired following a highly standardized protocol. Spirometry was also conducted on a subset. Individual-level 1- and 20-year average air pollution exposures were estimated using spatiotemporal models that included cohort-specific measurements. Multivariable regression was conducted to adjust for traditional risk factors and study location.
Results: Among 6,515 participants, we found evidence of an association between percent emphysema and long-term pollution concentrations in an analysis leveraging between-city exposure contrasts. Higher concentrations of PM2.5 (5 μg/m3) and NOx (25 ppb) over the previous year were associated with 0.6 (95% CI: 0.1, 1.2%) and 0.5 (95% CI: 0.1, 0.9%) higher average percent emphysema, respectively. However, after adjustment for study site the associations were –0.6% (95% CI: –1.5, 0.3%) for PM2.5 and –0.5% (95% CI: –1.1, 0.02%) for NOx. Lower lung function measures (FEV1 and FVC) were associated with higher PM2.5 and NOx levels in 3,791 participants before and after adjustment for study site, though most associations were not statistically significant.
Conclusions: Associations between ambient air pollution and percentage of emphysema-like lung were inconclusive in this cross-sectional study, thus longitudinal analyses may better clarify these associations with percent emphysema.
Citation: Adar SD, Kaufman JD, Diez-Roux AV, Hoffman EA, D’Souza J, Stukovsky KH, Rich SS, Rotter JI, Guo X, Raffel LJ, Sampson PD, Oron AP, Raghunathan T, Barr RG. 2015. Air pollution and percent emphysema identified by computed tomography in the Multi-Ethnic Study of Atherosclerosis. Environ Health Perspect 123:144–151; http://dx.doi.org/10.1289/ehp.1307951
Environmental Health Perspectives 10/2014; 123(2). DOI:10.1289/ehp.1307951 · 7.98 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] ABSTRACT: A sequential regression or chained equations imputation approach uses a Gibbs sampling type iterative algorithm which imputes the missing values using a sequence of conditional regression models. It is a flexible approach for handling different types of variables and complex data structures. Many simulation studies have shown that the multiple imputation inferences based on this procedure have desirable repeated sampling properties. However, a theoretical weakness of this approach is that the specification of a set of conditional regression models may not be compatible with a joint distribution of the variables being imputed. Hence, the convergence properties of the iterative algorithm are not well understood. This paper develops conditions for convergence and assesses the properties of inferences from both compatible and incompatible sequence of regression models. The results are established for the missing data pattern where each subject may be missing a value on at most one variable. The sequence of regression models are assumed to be empirically good fit for the data chosen by the imputer based on appropriate model diagnostics. The results are used to develop criteria for the choice of regression models.
Journal of the American Statistical Association 08/2014; 110(511):00-00. DOI:10.1080/01621459.2014.948117 · 1.98 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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; 176(10). DOI:10.1093/aje/kws182 · 5.23 Impact Factor
[Show abstract][Hide abstract] 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  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.
[Show abstract][Hide abstract] 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.
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.
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%.
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. DOI:10.1186/1472-6963-12-312 · 1.71 Impact Factor
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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 ] (tk−1,tk], conditional on being at risk at t k − 1 tk−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.
[Show abstract][Hide abstract] 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
[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