Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

DECOMPOSITION OF REGRESSION ESTIMATORS TO EXPLORE THE INFLUENCE OF "UNMEASURED" TIME-VARYING CONFOUNDERS

In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

PENALIZED FUNCTIONAL REGRESSION

We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast. Our methodology is motivated by a diffusion tensor imaging (DTI) study. The aim of this study is to analyze differences between various cerebral white matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations.

Placebo-controlled trials of Chinese herbal medicine and conventional medicine - comparative study

BACKGROUND: Chinese herbal medicine (CHM) is increasingly used in the West, but the evidence on its effectiveness is a matter of debate. We compared the characteristics, study quality and results of clinical trials of CHM and conventional medicine. METHODS: Comparative study of placebo-controlled trials of CHM and conventional medicine. Eleven bibliographic databases and searches by hand of 48 Chinese-language journals. Conventional medicine trials matched for condition and type of outcome were randomly selected from the Cochrane Controlled Trials Register (issue 1, 2003). Trials described as double-blind, with adequate generation of allocation sequence and adequate concealment of allocation, were assumed to be of high quality. Data were analysed using funnel plots and multivariable meta-regression models. RESULTS: 136 CHM trials (119 published in Chinese, 17 published in English) and 136 matched conventional medicine trials (125 published in English) were analysed. The quality of Chinese-language CHM trials tended to be lower than that of English-language CHM trials and conventional medicine trials. Three (2%) CHM trials and 10 (7%) conventional medicine trials were of high quality. In all groups, smaller trials showed more beneficial treatment effects than larger trials. CHM trials published in Chinese showed considerably larger effects than CHM trials published in English (adjusted ratio of ORs 0.29, 95% confidence intervals 0.17-0.52). CONCLUSIONS: Biases are present both in placebo-controlled trials of CHM and conventional medicine, but may be most pronounced in CHM trials published in Chinese-language journals. Only few CHM trials of adequate methodology exist and the effectiveness of CHM therefore remains poorly documented.

Asthma in young south Asian women living in the United Kingdom: the importance of early life

BACKGROUND: Studies of immigrants suggest that the environment during fetal life and duration of residence in the host country might influence the development of asthma. Little is known about the importance of the timing of the exposure in the host country and whether migrants might be especially vulnerable in certain age windows. OBJECTIVE: We compared the reported prevalence of asthma between young white and south Asian women in the United Kingdom, and investigated associations with country of birth and age at immigration. METHODS: A questionnaire on atopic disorders was posted to 2380 south Asian and 5796 white young mothers randomly sampled in Leicestershire. Data on ethnicity were also available from maternity records. Data were analysed using multivariable logistic regression and a propensity score approach. Results The reported prevalence of asthma was 10.9% in south Asian and 21.8% in white women. South Asian women who migrated to the United Kingdom aged 5 years or older reported less asthma (6.5%) than those born in the United Kingdom or who migrated before age 5 (16.0%), with an adjusted odds ratio of 0.38 [95% Confidence Interval 0.23-0.64, P<0.001]. For those who migrated aged over 5 years, the prevalence did not alter with the duration of residence in the United Kingdom. Current exposure to common environmental risk factors had relatively little effect on prevalence estimates. CONCLUSION: These data from a large population-based study support the hypothesis that early life environmental factors influence the risk of adult asthma.

Anemia is associated with abdominal aortic aneurysm (AAA) size and decreased long-term survival after endovascular AAA repair

OBJECTIVE: Anemia is a common comorbid condition in various inflammatory states and an established predictor of mortality in patients with chronic heart failure, ischemic heart disease, and end-stage renal disease. The present study of patients with abdominal aortic aneurysm (AAA) undergoing endovascular repair (EVAR) assessed the relationships between baseline hemoglobin concentration and AAA size, as well as anemia and long-term survival. METHODS: Between March 1994 and November 2006, 711 patients (65 women, mean age 75.8 +/- 7.8 years) underwent elective EVAR. Anemia was defined as a hemoglobin level <13 g/dL in men and <12 g/dL in women. Post-EVAR mean follow-up was 48.3 +/- 32.0 months. Association of hemoglobin level with AAA size was assessed with multiple linear regression. Mortality was determined with use of the internet-based Social Security Death Index and the electronic hospital record. Kaplan-Meier survival curves of anemic and nonanemic patient groups were compared by the log-rank method. Multivariable logistic regression models were used to determine the influence of anemia on vital status after EVAR. RESULTS: A total of 218/711 (30.7%) of AAA patients undergoing EVAR had anemia at baseline. After adjustment for various risk factors, hemoglobin level was inversely related to maximum AAA diameter (beta: - .144, 95%-CI: -1.482 - .322, P = .002). Post-EVAR survival was 65.5% at 5 years and 44.4% at 10 years. In long-term follow-up, survival was significantly lower in patients with anemia as compared to patients without anemia (P < .0001 by log-rank). Baseline hemoglobin levels were independently related to long-term mortality in multivariable Cox regression analysis adjusted for various risk factors (adjusted HR: 0.866, 95% CI: .783 to .958, P = .005). Within this model, statin use (adjusted HR: .517, 95% CI: .308 to .868, P = .013) was independently related to long-term survival, whereas baseline AAA diameter (adjusted HR: 1.022, 95% CI: 1.009 to 1.036, P = .001) was an independently associated with increased mortality. CONCLUSIONS: Baseline hemoglobin concentration is independently associated with AAA size and reduced long-term survival following EVAR. Thus, the presence or absence of anemia offers a potential refinement of existing risk stratification instruments.

Association of lipopolysaccharide-binding protein and coronary artery disease in men

OBJECTIVES: In this study we tested the hypothesis that lipopolysaccharide-binding protein (LBP) might be able to be used as a biomarker for coronary artery disease (CAD). BACKGROUND: The mechanisms by which the innate immune recognition of pathogens could lead to atherosclerosis remain unclear. Lipopolysaccharide-binding protein is the first protein to encounter lipopolysaccharide and to deliver it to its cellular targets, toll-like receptors; therefore, its presence might be a reliable biomarker that indicates activation of innate immune responses. METHODS: A total of 247 men undergoing elective coronary angiography were studied, and the extent of coronary atherosclerosis was assessed by 2 established scores: "extent score" and "severity score." Levels of LBP, markers of inflammation, and traditional risk factors for CAD were assessed. RESULTS: Serum LBP concentration was significantly increased in 172 patients with angiographically confirmed CAD compared with 75 individuals without coronary atherosclerosis (20.6 +/- 8.7 pg/ml vs. 17.1 +/- 6.0 pg/ml, respectively; p = 0.002). Moreover in multivariable logistic regression analyses, adjusted for established cardiovascular risk factors and markers of systemic inflammation, LBP was a significant and independent predictor of prevalent CAD (p < 0.05 in all models). CONCLUSIONS: Lipopolysaccharide-binding protein might serve as a novel marker for CAD in men. The present results underlie the potential importance of innate immune mechanisms for CAD. Further studies are warranted to bolster the data and to identify pathogenetic links between innate immune system activation and atherosclerosis.

Dose-dependent influence of didanosine on immune recovery in HIV-infected patients treated with tenofovir

BACKGROUND: Antiretroviral therapy (ART) containing tenofovir disoproxil fumarate (TDF) and didanosine (ddI) has been associated with poor immune recovery despite virologic success. This effect might be related to ddI toxicity since ddI exposure is substantially increased by TDF. OBJECTIVE: To analyze whether immune recovery during ART with TDF and ddI is ddI-dose dependent. DESIGN AND METHODS: A retrospective longitudinal analysis of immune recovery measured by the CD4 T-cell slope in 614 patients treated with ART containing TDF with or without ddI. Patients were stratified according to the tertiles of their weight-adjusted ddI dose: low dose (< 3.3 mg/kg), intermediate dose (3.3-4.1 mg/kg) and high dose (> 4.1 mg/kg). Cofactors modifying the degree of immune recovery after starting TDF-containing ART were identified by univariable and multivariable linear regression analyses. RESULTS: CD4 T-cell slopes were comparable between patients treated with TDF and a weight-adjusted ddI-dose of < 4.1 mg/kg per day (n = 143) versus TDF-without-ddI (n = 393). In the multivariable model the slopes differed by -13 CD4 T cells/mul per year [95% confidence interval (CI), -42 to 17; P = 0.40]. In contrast, patients treated with TDF and a higher ddI dose (> 4.1 mg/kg per day, n = 78) experienced a significantly impaired immune recovery (-47 CD4 T cells/microl per year; 95% CI, -82 to -12; P = 0.009). The virologic response was comparable between the different treatment groups. CONCLUSIONS: Immune recovery is impaired, when high doses of ddI (> 4.1 mg/kg) are given in combination with TDF. If the dose of ddI is adjusted to less than 4.1 mg/kg per day, immune recovery is similar to other TDF-containing ART regimen.