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.

An open-label, noncomparative phase II trial to evaluate the efficacy and safety of docetaxel in combination with gefitinib in patients with hormone-refractory metastatic prostate cancer

BACKGROUND: Prostate cancer is the most common type of cancer in men, however, therapeutic options are limited. 50-90% of hormone-refractory prostate cancer cells show an overexpression of epidermal growth factor receptor (EGFR), which may contribute to uncontrolled proliferation and resistance to chemotherapy. In vitro, gefitinib, an orally administered tyrosine kinase inhibitor, has shown a significant increase in antitumor activity when combined with chemotherapy. PATIENTS AND METHODS: In this phase II study, the safety and efficacy of gefitinib in combination with docetaxel, a chemotherapeutic agent commonly used for prostate cancer, was investigated in patients with hormone-refractory prostate cancer (HRPC). 37 patients with HRPC were treated continuously with gefitinib 250 mg once daily and docetaxel 35 mg/m2 i.v. for up to 6 cycles. PSA response, defined as a =50% decrease in serum PSA compared with trial entry, was the primary efficacy parameter. PSA levels were measured at prescribed intervals. RESULTS: The response rate and duration of response were consistent with those seen with docetaxel monotherapy. The combination of docetaxel and gefitinib was reasonably well tolerated in this study. CONCLUSION: Future studies should investigate whether patients with specific tumor characteristics, e.g. EGFR protein overexpression, respond better to gefitinib than patients without, leading to a more customized therapy option.

Randomised controlled trials of homeopathy in hyperactive children: treatment procedure leads to an unconventional study design. Experience with open-label homeopathic treatment preceding the Swiss ADHD placebo controlled, randomised, double-blind, cross-over trial

BACKGROUND: Treatment of patients with attention deficit hyperactivity disorder (ADHD) with homeopathy is difficult. The Swiss randomised, placebo controlled, cross-over trial in ADHD patients (Swiss ADHD trial) was designed with an open-label screening phase prior to the randomised controlled phase. During the screening phase, the response of each child to successive homeopathic medications was observed until the optimal medication was identified. Only children who reached a predefined level of improvement participated in the randomised, cross-over phase. Although the randomised phase revealed a significant beneficial effect of homeopathy, the cross-over caused a strong carryover effect diminishing the apparent difference between placebo and verum treatment. METHODS: This retrospective analysis explores the screening phase data with respect to the risk of failure to demonstrate a specific effect of a randomised controlled trial (RCT) with randomisation at the start of the treatment. RESULTS: During the screening phase, 84% (70/83) of the children responded to treatment and reached eligibility for the randomised trial after a median time of 5 months (range 1-18), with a median of 3 different medications (range 1-9). Thirteen children (16%) did not reach eligibility. Five months after treatment start, the difference in Conners Global Index (CGI) rating between responders and non-responders became highly significant (p = 0.0006). Improvement in CGI was much greater following the identification of the optimal medication than in the preceding suboptimal treatment period (p < 0.0001). CONCLUSIONS: Because of the necessity of identifying an optimal medication before response to treatment can be expected, randomisation at the start of treatment in an RCT of homeopathy in ADHD children has a high risk of failure to demonstrate a specific treatment effect, if the observation time is shorter than 12 months.

Change in pain severity with open label venlafaxine use in patients with a depressive symptomatology: an observational study in primary care

PURPOSE: Venlafaxine has shown benefit in the treatment of depression and pain. Worldwide data are extensively lacking investigating the outcome of chronic pain patients with depressive symptoms treated by venlafaxine in the primary care setting. This observational study aimed to elucidate the efficacy of venlafaxine and its prescription by Swiss primary care physicians and psychiatrists in patients with chronic pain and depressive symptomatology. SUBJECTS AND METHODS: We studied 505 patients with depressive symptoms suffering from chronic pain in a prospective naturalistic Swiss community based observational trial with venlafaxine in primary care. These patients have been treated with venlafaxine by 122 physicians, namely psychiatrists, general practitioners, and internists. RESULTS: On average, patients were treated with 143+/-75 mg (0-450 mg) venlafaxine daily for a follow-up of three months. Venlafaxine proved to be beneficial in the treatment of both depressive symptoms and chronic pain. DISCUSSION: Although side effects were absent in most patients, physicians might have frequently omitted satisfactory response rate of depression by underdosing venlafaxine. Our results reflect the complexity in the treatment of chronic pain in patients with depressive symptoms in primary care. CONCLUSION: Further randomized dose-finding studies are needed to learn more about the appropriate dosage in treating depression and comorbid pain with venlafaxine.

On-chip non-invasive and label-free cell discrimination by impedance spectroscopy

OBJECTIVES: Many flow-cytometric cell characterization methods require costly markers and colour reagents. We present here a novel device for cell discrimination based on impedance measurement of electrical cell properties in a microfluidic chip, without the need of extensive sample preparation steps and the requirement of labelling dyes. MATERIALS AND METHODS, RESULTS: We demonstrate that in-flow single cell measurements in our microchip allow for discrimination of various cell line types, such as undifferentiated mouse fibroblasts 3T3-L1 and adipocytes on the one hand, or human monocytes and in vitro differentiated dendritic cells and macrophages on the other hand. In addition, viability and apoptosis analyses were carried out successfully for Jurkat cell models. Studies on several species, including bacteria or fungi, demonstrate not only the capability to enumerate these cells, but also show that even other microbiological life cycle phases can be visualized. CONCLUSIONS: These results underline the potential of impedance spectroscopy flow cytometry as a valuable complement to other known cytometers and cell detection systems.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.