An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction.

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.

Tensor response quantile regression with neuroimaging data.

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.

Diagnostic evaluation of pharmacokinetic features of functional markers.

The dynamicity of functional (curve) markers from modern clinical studies offers deeper insights into complex disease physiology. A frequent clinical practice is to examine various 'pharmacokinetic features' of functional markers (definite integral, maximum value, time to maximum, etc.) that reflect important physiological underpinnings. For instance, the current diagnostic procedure for kidney obstruction is to examine several pharmacokinetic features of renogram curves characterizing renal function. Motivated by such clinical practices, we develop a statistical framework for evaluating diagnostic accuracy of pharmacokinetic features using area under the receiver operating characteristic curve (AUC). The major challenge is that functional markers are observed at discrete time points with measurement error. To address this challenge, we develop a two-stage non-parametric AUC estimator based on summary functionals providing unified representation of various pharmacokinetic features and study its asymptotic properties. We also propose a sensible adaptation of a semiparametric regression model that can describe heterogeneity of AUC across different subpopulations, while appropriately handling discreteness and noise in observed functional markers. Here, a novel data-driven approach that balances between bias and efficiency of the regression coefficient estimates is introduced. Finally, the framework is applied to rigorously evaluate pharmacokinetic features of renogram curves potentially useful for detecting kidney obstruction.

Gamma models for estimating the odds ratio for a skewed biomarker measured in pools and subject to errors.

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.

Differences in incident and recurrent myocardial infarction among White and Black individuals aged 35 to 84: Findings from the ARIC community surveillance study.

BACKGROUND: No previous study has examined racial differences in recurrent acute myocardial infarction (AMI) in a community population. We aimed to examine racial differences in recurrent AMI risk, along with first AMI risk in a community population. METHODS: The community surveillance of the Atherosclerosis Risk in Communities Study (2005-2014) included 470,000 people 35 to 84 years old in 4 U.S. communities. Hospitalizations for recurrent and first AMI were identified from ICD-9-CM discharge codes. Poisson regression models were used to compare recurrent and first AMI risk ratios between Black and White residents. RESULTS: Recurrent and first AMI risk per 1,000 persons were 8.8 (95% CI, 8.3-9.2) and 20.7 (95% CI, 20.0-21.4) in Black men, 6.8 (95% CI, 6.5-7.0) and 14.1 (95% CI, 13.8-14.5) in White men, 5.3 (95% CI, 5.0-5.7) and 16.2 (95% CI, 15.6-16.8) in Black women, and 3.1 (95% CI, 3.0-3.3) and 8.8 (95% CI, 8.6-9.0) in White women, respectively. The age-adjusted risk ratios (RR) of recurrent AMI were higher in Black men vs White men (RR, 1.58 95% CI, 1.30-1.92) and Black women vs White women (RR, 2.09 95% CI, 1.64-2.66). The corresponding RRs were slightly lower for first AMI: Black men vs White men, RR, 1.49 (95% CI, 1.30-1.71) and Black women vs White women, RR, 1.65 (95% CI, 1.42-1.92) CONCLUSIONS: Large disparities exist by race for recurrent AMI risk in the community. The magnitude of disparities is stronger for recurrent events than for first events, and particularly among women.

A Bayesian multiple imputation approach to bivariate functional data with missing components.

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.

Nonparametric estimation of broad sense agreement between ordinal and censored continuous outcomes.

The concept of broad sense agreement (BSA) has recently been proposed for studying the relationship between a continuous measurement and an ordinal measurement. They developed a nonparametric procedure for estimating the BSA index, which is only applicable to completely observed data. In this work, we consider the problem of evaluating BSA index when the continuous measurement is subject to censoring. We propose a nonparametric estimation method built upon a derivation of a new functional representation of the BSA index, which allows for accommodating censoring by plugging in the nonparametric survival function estimators. We establish the consistency and asymptotic normality for the proposed BSA estimator. We also investigate an alternative approach based on the strategy of multiple imputation, which is shown to have better empirical performance with small sample sizes than the plug-in method. Extensive simulation studies are conducted to evaluate our proposals. We illustrate our methods via an application to a Surgical Intensive Care Unit study.

A New Functional Representation of Broad Sense Agreement.

We derive a new functional representation of Broad Sense Agreement (BSA) index that evaluates the agreement/alignment between a continuous measurement and an ordinary measurement. Using this result, we develop an alternative BSA estimator, which can offer significant numerical advantages.

Polybrominated Biphenyl Exposure and Menstrual Cycle Function.

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.

Assessing alignment between functional markers and ordinal outcomes based on broad sense agreement.

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.

A Smooth Nonparametric Approach to Determining Cut-Points of A Continuous Scale.

The problem of determining cut-points of a continuous scale according to an establish categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, or facilitating interpretations. A general analytic framework was recently proposed for assessing optimal cut-points defined based on some pre-specified criteria. However, the implementation of the existing nonparametric estimators under this framework and the associated inferences can be computationally intensive when more than a few cut-points need to be determined. To address this important issue, a smoothing-based modification of the current method is proposed and is found to substantially improve the computational speed as well as the asymptotic convergence rate. Moreover, a plug-in type variance estimation procedure is developed to further facilitate the computation. Extensive simulation studies confirm the theoretical results and demonstrate the computational benefits of the proposed method. The practical utility of the new approach is illustrated by an application to a mental health study.

A local agreement pattern measure based on hazard functions for survival outcomes.

Assessing agreement is often of interest in biomedical and clinical research when measurements are obtained on the same subjects by different raters or methods. Most classical agreement methods have been focused on global summary statistics, which cannot be used to describe various local agreement patterns. The objective of this work is to study the local agreement pattern between two continuous measurements subject to censoring. In this article, we propose a new agreement measure based on bivariate hazard functions to characterize the local agreement pattern between two correlated survival outcomes. The proposed measure naturally accommodates censored observations, fully captures the dependence structure between bivariate survival times and provides detailed information on how the strength of agreement evolves over time. We develop a nonparametric estimation method for the proposed local agreement pattern measure and study theoretical properties including strong consistency and asymptotical normality. We then evaluate the performance of the estimator through simulation studies and illustrate the method using a prostate cancer data example.

Overall indices for assessing agreement among multiple raters.

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.

Logistic regression with a continuous exposure measured in pools and subject to errors.

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.

99mTc-MAG3: Image Wisely.

Purpose To determine if commonly administered doses of technetium 99m (99mTc) mertiatide (MAG3) in the range of 300-370 MBq (approximately 8-10 mCi) contribute to image interpretation and justify the resulting radiation exposure. Materials and Methods The respective institutional review boards approved this HIPAA-compliant study and waived informed consent. Baseline and furosemide 99mTc-MAG3 imaging examinations in 50 patients suspected of having renal obstruction and 48 patients suspected of having renovascular hypertension (RVH) were randomly selected from archived databases and were independently scored by three experienced readers without access to 2-second flow images. Readers were blinded to their original scores, and then they rescored each examination with access to high-activity 2-second flow images. Relative renal function was determined after a low activity (62.9 MBq ± 40.7) baseline acquisition for RVH and a high activity (303.4 MBq ± 48.1) acquisition after administration of enalaprilat. Data were analyzed by using random effects analysis of variance and mean and standard error of the mean for the difference between sets of scores and the difference between relative function measurements. Results There was no significant difference in the scores without flow images compared with blinded scores with high-activity flow images for patients suspected of having obstruction (P = .80) or RVH (P = .24). Moreover, there was no significant difference in the relative uptake measurements after administration of low and high activities (P > .99). Conclusion Administered doses of 99mTc-MAG3 in the range of 300-370 MBq (approximately 8-10 mCi) do not affect the relative function measurements or contribute to interpretation of images in patients suspected of having RVH or obstruction compared with administration of lower doses; unnecessary radiation exposure can be avoided by administering doses in the range of 37-185 MBq as recommended incurrent guidelines. © RSNA, 2017.

A joint modeling approach for multivariate survival data with random length.

In many biomedical studies that involve correlated data, an outcome is often repeatedly measured for each individual subject along with the number of these measurements, which is also treated as an observed outcome. This type of data has been referred as multivariate random length data by Barnhart and Sampson (1995). A common approach to handling such type of data is to jointly model the multiple measurements and the random length. In previous literature, a key assumption is the multivariate normality for the multiple measurements. Motivated by a reproductive study, we propose a new copula-based joint model which relaxes the normality assumption. Specifically, we adopt the Clayton-Oakes model for multiple measurements with flexible marginal distributions specified as semi-parametric transformation models. The random length is modeled via a generalized linear model. We develop an approximate EM algorithm to derive parameter estimators and standard errors of the estimators are obtained through bootstrapping procedures and the finite-sample performance of the proposed method is investigated using simulation studies. We apply our method to the Mount Sinai Study of Women Office Workers (MSSWOW), where women were prospectively followed for 1 year for studying fertility.

Nonparametric Regression Method for Broad Sense Agreement.

Characterizing the correspondence between an ordinal measurement and a continuous measurement is often of interest in mental health studies. To this end, Peng, Li, Guo, and Manatunga (2011) introduced the concept of broad sense agreement (BSA) and developed nonparametric estimation and inference for a BSA measure. In this work, we propose a non-parametric regression framework for BSA, which provides a robust tool to further investigate population heterogeneity in BSA. We develop inferential procedures including regression function estimation and hypothesis testing. Extensive simulation studies demonstrate satisfactory performance of the proposed method. We also apply the new method to a recent Grady Trauma Study and reveal an interesting impact of depression severity on the alignment between a self-reported symptom instrument and clinician diagnosis in posttraumatic stress disorder (PSTD) patients.

Efficacy and Safety of Glutamine-supplemented Parenteral Nutrition in Surgical ICU Patients: An American Multicenter Randomized Controlled Trial.

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 (n = 75) 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 bloodstream infection 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 events were similar. CONCLUSIONS: PN supplemented with GLN dipeptide was safe, but did not alter clinical outcomes among SICU patients.

Quantile regression analysis of censored longitudinal data with irregular outcome-dependent follow-up.

In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.