Search

Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction. [ABSTRACT FROM AUTHOR]

Modern medical devices are increasingly producing complex data that could offer deeper insights into physiological mechanisms of underlying diseases. One type of complex data that arises frequently in medical imaging studies is functional data, whose sampling unit is a smooth continuous function. In this work, with the goal of establishing the scientific validity of experiments involving modern medical imaging devices, we focus on the problem of evaluating reliability and reproducibility of multiple functional data that are measured on the same subjects by different methods (i.e. different technologies or raters). Specifically, we develop a series of intraclass correlation coefficient and concordance correlation coefficient indices that can assess intra-method, inter-method, and total (intra + inter) agreement based on multivariate multilevel functional data consisting of replicated functional data measurements produced by each of the different methods. For efficient estimation, the proposed indices are expressed using variance components of a multivariate multilevel functional mixed effect model, which can be smoothly estimated by functional principal component analysis. Extensive simulation studies are performed to assess the finite-sample properties of the estimators. The proposed method is applied to evaluate the reliability and reproducibility of renogram curves produced by a high-tech radionuclide image scan used to non-invasively detect kidney obstruction. [ABSTRACT FROM AUTHOR]

Pretreatment with the antidepressant drug Paxil can reduce the risk of depression in cancer patients who take interferon alfa-2b. Depression is a known side effect of interferon alfa-2b treatment.

Prentice reviews various existing methods for multivariate failure time regression data and applies the recently developed multivariate marginal hazard methods to the Women's Health Initiative hormone therapy trial data. We feel privileged to present this special issue to honor David Oakes, Professor of Biostatistics and Computational Biology at University of Rochester. [Extracted from the article]

Existing missing data methods for functional data mainly focus on reconstructing missing measurements along a single function—a univariate functional data setting. Motivated by a renal study, we focus on a bivariate functional data setting, where each sampling unit is a collection of two distinct component functions, one of which may be missing. Specifically, we propose a Bayesian multiple imputation approach based on a bivariate functional latent factor model that exploits the joint changing patterns of the component functions to allow accurate and stable imputation of one component given the other. We further extend the framework to address multilevel bivariate functional data with missing components by modeling and exploiting inter‐component and intra‐subject correlations. We develop a Gibbs sampling algorithm that simultaneously generates multiple imputations of missing component functions and posterior samples of model parameters. For multilevel bivariate functional data, a partially collapsed Gibbs sampler is implemented to improve computational efficiency. Our simulation study demonstrates that our methods outperform other competing methods for imputing missing components of bivariate functional data under various designs and missingness rates. The motivating renal study aims to investigate the distribution and pharmacokinetic properties of baseline and post‐furosemide renogram curves that provide further insights into the underlying mechanism of renal obstruction, with post‐furosemide renogram curves missing for some subjects. We apply the proposed methods to impute missing post‐furosemide renogram curves and obtain more refined insights. [ABSTRACT FROM AUTHOR]

Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Functional markers and their quantitative features (eg, maximum value, time to maximum, area under the curve [AUC], etc) are increasingly being used in clinical studies to diagnose diseases. It is thus of interest to assess the diagnostic utility of functional markers by assessing alignment between their quantitative features and an ordinal gold standard test that reflects the severity of disease. The concept of broad sense agreement (BSA) has recently been introduced for studying the relationship between continuous and ordinal measurements, and provides a promising tool to address such a question. Our strategy is to adopt a general class of summary functionals (SFs), each of which flexibly captures a different quantitative feature of a functional marker, and study its alignment according to an ordinal outcome via BSA. We further illustrate the proposed framework using three special classes of SFs (AUC‐type, magnitude‐specific, and time‐specific) that are widely used in clinical settings. The proposed BSA estimator is proven to be consistent and asymptotically normal given a consistent estimator for the SF. We further provide an inferential framework for comparing a pair of candidate SFs in terms of their importance on the ordinal outcome. Our simulation results demonstrate satisfactory finite‐sample performance of the proposed framework. We demonstrate the application of our methods using a renal study. [ABSTRACT FROM AUTHOR]

Background: Brominated flame retardants, including polybrominated biphenyls (PBB), are persistent compounds reported to affect sex hormones in animals; less is known about potential effects in humans. An industrial accident in 1973-1974 exposed Michigan residents to PBB through contaminated food. We examined whether this exposure to PBB had long-term effects on menstrual cycle function.Methods: In 2004-2006, we recruited reproductive-aged women in the Michigan PBB Registry who were not pregnant, lactating, or taking hormonal medications. Participants kept daily diaries and provided daily urine samples for up to 6 months. We assayed the urine samples for estrone 3-glucuronide (E13G), pregnanediol 3-glucuronide (Pd3G), and follicle stimulating hormone (FSH). We fit linear mixed models among women aged 35-42 years to describe the relation between serum PBB levels and log-transformed, creatinine-adjusted daily endocrine levels among women who were premenarchal during the exposure incident in 1973-1974 (n = 70).Results: We observed that high (>3.0 parts per billion [ppb]) and medium (>1.0-3.0 ppb) PBB exposure were associated with lower E13G levels across the menstrual cycle and lower FSH levels during the follicular phase, compared with low PBB exposure (≤1.0 ppb). High PBB exposure was also associated with lower Pd3G levels across the cycle compared with low PBB exposure, whereas Pd3G levels were similar in women with medium and low PBB exposure.Conclusion: Our results are consistent with a hypothesized effect of exposure to an exogenous estrogen agonist but the modest sample size of the study requires cautious interpretation. [ABSTRACT FROM AUTHOR]

The need to assess agreement exists in various clinical studies where quantifying inter‐rater reliability is of great importance. Use of unscaled agreement indices, such as total deviation index and coverage probability (CP), is recommended for two main reasons: (i) they are intuitive in a sense that interpretations are tied to the original measurement unit; (ii) practitioners can readily determine whether the agreement is satisfactory by directly comparing the value of the index to a prespecified tolerable CP or absolute difference. However, the unscaled indices were only defined in the context of comparing two raters or multiple raters that assume homogeneity of variances across raters. In this paper, we introduce a set of overall indices based on the root mean square of pairwise differences that are unscaled and can be used to evaluate agreement among multiple raters that often exhibit heterogeneous measurement processes in practice. Furthermore, we propose another overall agreement index based on the root mean square of pairwise differences that is scaled and extends the concept of the recently proposed relative area under CP curve in the presence of multiple raters. We present the definitions of overall indices and propose inference procedures in which bootstrap methods are used for the estimation of standard errors. We assess the performance of the proposed approach and demonstrate its superiority over the existing methods when raters exhibit heterogeneous measurement processes using simulation studies. Finally, we demonstrate the application of our methods using a renal study. [ABSTRACT FROM AUTHOR]

In a multivariable logistic regression setting where measuring a continuous exposure requires an expensive assay, a design in which the biomarker is measured in pooled samples from multiple subjects can be very cost effective. A logistic regression model for poolwise data is available, but validity requires that the assay yields the precise mean exposure for members of each pool. To account for errors, we assume the assay returns the true mean exposure plus a measurement error (ME) and/or a processing error (PE). We pursue likelihood-based inference for a binary health-related outcome modeled by logistic regression coupled with a normal linear model relating individual-level exposure to covariates and assuming that the ME and PE components are independent and normally distributed regardless of pool size. We compare this approach with a discriminant function-based alternative, and we demonstrate the potential value of incorporating replicates into the study design. Applied to a reproductive health dataset with pools of size 2 along with individual samples and replicates, the model fit with both ME and PE had a lower AIC than a model accounting for ME only. Relative to ignoring errors, this model suggested a somewhat higher (though still nonsignificant) adjusted log-odds ratio associating the cytokine MCP-1 with risk of spontaneous abortion. Simulations modeled after these data confirm validity of the methods, demonstrate how ME and particularly PE can reduce the efficiency advantage of a pooling design, and highlight the value of replicates in improving stability when both errors are present. [ABSTRACT FROM AUTHOR]

Objective: To determine whether glutamine (GLN)-supplemented parenteral nutrition (PN) improves clinical outcomes in surgical intensive care unit (SICU) patients. Summary Background Data: GLN requirements may increase with critical illness. GLN-supplemented PN may improve clinical outcomes in SICU patients. Methods: A parallel-group, multicenter, double-blind, randomized, controlled clinical trial in 150 adults after gastrointestinal, vascular, or cardiac surgery requiring PN and SICU care. Patients were without significant renal or hepatic failure or shock at entry. All received isonitrogenous, isocaloric PN [1.5 g/kg/d amino acids (AAs) and energy at 1.3? estimated basal energy expenditure]. Controls (n=75) received standard GLN-free PN (STD-PN); the GLN group (n75) received PN containing alanyl-GLN dipeptide (0.5 g/kg/d), proportionally replacing AA in PN (GLN-PN). Enteral nutrition (EN) was advanced and PN weaned as indicated. Hospital mortality and infections were primary endpoints. Results: Baseline characteristics, days on study PN and daily macronutrient intakes via PN and EN, were similar between groups. There were 11 hospital deaths (14.7%) in the GLN-PN group and 13 deaths in the STD-PN group (17.3%; difference, -2.6%; 95% confidence interval, -14.6% to 9.3%; P=0.66). The 6-month cumulative mortality was 31.4% in the GLN-PN group and 29.7% in the STD-PN group (P 0.88). Incident bloodstreaminfection rate was 9.6 and 8.4 per 1000 hospital days in the GLN-PN and STD-PN groups, respectively (P=0.73).Other clinical outcomes and adverse eventswere similar. Conclusions: PN supplemented with GLN dipeptide was safe, but did not alter clinical outcomes among SICU patients. [ABSTRACT FROM AUTHOR]

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) spectroscopy, traditionally used in analytical chemistry, has recently been introduced to studies of metabolite composition of biological fluids and tissues. Metabolite levels change over time, and providing a tool for better extraction of NMR peaks exhibiting periodic behavior is of interest. We propose a method in which NMR peaks are clustered based on periodic behavior. Periodic regression is used to obtain estimates of the parameter corresponding to period for individual NMR peaks. A mixture model is then used to develop clusters of peaks, taking into account the variability of the regression parameter estimates. Methods are applied to NMR data collected from human blood plasma over a 24-h period. Simulation studies show that the extra variance component due to the estimation of the parameter estimate should be accounted for in the clustering procedure. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 579-589, 2011 [ABSTRACT FROM AUTHOR]

When the prediction of subject-specific random effects is of interest, constrained Bayes predictors (CB) have been shown to reduce the shrinkage of the widely accepted Bayes predictor while still maintaining desirable properties, such as optimizing mean-square error subsequent to matching the first two moments of the random effects of interest. However, occupational exposure and other epidemiologic (e.g. HIV) studies often present a further challenge because data may fall below the measuring instrument's limit of detection. Although methodology exists in the literature to compute Bayes estimates in the presence of non-detects (BayesND), CB methodology has not been proposed in this setting. By combining methodologies for computing CBs and BayesND, we introduce two novel CBs that accommodate an arbitrary number of observable and non-detectable measurements per subject. Based on application to real data sets (e.g. occupational exposure, HIV RNA) and simulation studies, these CB predictors are markedly superior to the Bayes predictor and to alternative predictors computed using ad hoc methods in terms of meeting the goal of matching the first two moments of the true random effects distribution. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The menstrual cycle is an important indicator of underlying hormonal function. Although menstrual cycle variability (sometimes referred to as 'regularity') is associated with a variety of demographic, behavioral, occupational, and environmental factors, as well as with several chronic diseases, few studies have examined its association with fecundity. We investigated whether a woman's menstrual cycle variability was associated with the likelihood of her achieving pregnancy. In this prospective study, we analyzed 3,536 menstrual cycles from 401 women (aged 19-41) recruited from 1990-1994. The women provided daily diaries recording menstrual bleeding, intercourse, and birth control use. Urine samples were assayed for human chorionic gonadotropin to identify early pregnancies during each menstrual cycle. Each woman's menstrual cycle variability was defined by the standard deviation of her cycle lengths during followup. The median follow-up was eight cycles. The outcome was her per-cycle probability of pregnancy. We found that women with high menstrual cycle variability had a reduced (51% lower) per cycle probability of pregnancy (fecundity ratio: 0.49; 95% confidence interval: 0.31, 0.77) compared with women with minimal variability. This relationship was independent of a woman's age and her mean cycle length. Thus, researchers and clinicians using menstrual cycle characteristics as indicators of endocrine or reproductive health should include measures of cycle variability in addition to the more commonly examined cycle length. [ABSTRACT FROM AUTHOR]

The Michigan Female Health Study (MFHS) conducted research focusing on reproductive health outcomes among women exposed to polybrominated biphenyls (PBBs). In the work presented here, the available longitudinal serum PBB exposure measurements are used to obtain predictions of PBB exposure for specific time points of interest via random effects models. In a two-stage approach, a prediction of the PBB exposure is obtained and then used in a second-stage health outcome model. This paper illustrates how a unified approach, which links the exposure and outcome in a joint model, provides an efficient adjustment for covariate measurement error. We compare the use of empirical Bayes predictions in the two-stage approach with results from a joint modeling approach, with and without an adjustment for left- and interval-censored data. The unified approach with the adjustment for left- and interval-censored data resulted in little bias and near-nominal confidence interval coverage in both the logistic and linear model setting. Published in 2010 by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The analysis of data from epidemiologic and environmental studies presents challenges such as skewness of distribution, rounding and multiple measurements over time. To model trends over time based on repeated measurements, we propose a general latent model suitable for highly skewed data. The model assumes that the observed outcome is determined by an unobservable outcome that follows a Weibull distribution. To accommodate correlations among repeated responses over time, we introduce a general random effect from the power variance function (PVF) family of distributions, including the gamma distribution often employed in the literature. The resulting marginal likelihood has a closed form without resorting to numerical or approximation methods. We study estimation and hypothesis testing under these models, with different choices of random effect distributions. Simulation studies are conducted to evaluate their performance. Finally, we apply the proposed method to exposure data collected from the Michigan polybrominated biphenyl (MIPBB) study. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The prediction of random effects corresponding to subject-specific characteristics (e.g. means or rates of change) can be very useful in medical and epidemiologic research. At times, one may be most interested in obtaining accurate and/or precise predictions for subjects whose characteristic places them in a tail of the distribution. While the typical posterior mean predictor dominates others in terms of overall mean squared error of prediction (MSEP), its tendency to 'overshrink' has motivated research into alternatives emphasizing other criteria. Here, we specifically target MSEP within a certain region (e.g. above a known cut-off for high risk or a specified percentile of the random effect distribution), and we consider minimizing this quantity with and without constraints on overall MSEP efficiency. We use the normal-theory random intercept model to derive prediction methods with potential to yield markedly better performance for subjects in the specified region, given a well-controlled and (if desired) modest concession of overall MSEP. Criteria geared toward classification as well as overall and regional prediction unbiasedness are also provided. We evaluate the proposed techniques and illustrate them using repeated measures data on fasting blood glucose from type 2 diabetes patients. A simulation study verifies that theoretical properties and relative performances of the proposed predictors are essentially maintained when calculating them in practice based on estimated mixed linear model parameters. Straightforward extensions to incorporate covariates and additional random effects are briefly outlined. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]