CBCRisk-Black: a personalized contralateral breast cancer risk prediction model for black women.

PURPOSE: Black breast cancer (BC) survivors have a higher risk of developing contralateral breast cancer (CBC) than Whites. Existing CBC risk prediction tools are developed based on mostly White women. To address this racial disparity, it is crucial to develop tools tailored for Black women to help them inform about their actual risk of CBC. METHODS: We propose an absolute risk prediction model, CBCRisk-Black, specifically for Black BC patients. It uses data on Black women from two sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER). First, a matched lasso logistic regression model for estimating relative risks (RR) is developed. Then, it is combined with relevant hazard rates and attributable risks to obtain absolute risks. Six-fold cross-validation is used to internally validate CBCRisk-Black. We also compare CBCRisk-Black with CBCRisk, an existing CBC risk prediction model. RESULTS: The RR model uses data from BCSC on 744 Black women (186 cases). CBCRisk-Black has four risk factors (RR compared to baseline): breast density (2.13 for heterogeneous/extremely dense), family history of BC (2.28 for yes), first BC tumor size (2.14 for T3/T4, 1.56 for TIS), and age at first diagnosis of BC (1.41 for < 40). The area under the receiver operating characteristic curve (AUC) for 3- and 5-year predictions are 0.72 and 0.65 for CBCRisk-Black while those are 0.65 and 0.60 for CBCRisk. CONCLUSION: CBCRisk-Black may serve as a useful tool to clinicians in counseling Black BC patients by providing a more accurate and personalized CBC risk estimate.

A segmented measurement error model for modeling and analysis of method comparison data.

Method comparison studies are concerned with estimating relationship between two clinical measurement methods. The methods often exhibit a structural change in the relationship over the measurement range. Ignoring this change would lead to an inaccurate estimate of the relationship. Motivated by a study of two digoxin assays where such a change occurs, this article develops a statistical methodology for appropriately analyzing such studies. Specifically, it proposes a segmented extension of the classical measurement error model to allow a piecewise linear relationship between the methods. The changepoint at which the transition takes place is treated as an unknown parameter in the model. An expectation-maximization-type algorithm is developed to fit the model and appropriate extensions of the existing measures are proposed for segment-specific evaluation of similarity and agreement. Bootstrapping and large-sample theory of maximum likelihood estimators are employed to perform the relevant inferences. The proposed methodology is evaluated by simulation and is illustrated by analyzing the digoxin data.

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies.

BACKGROUND: Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. METHODS: We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference. RESULTS: We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers. CONCLUSION: Our method can be helpful in detecting pathway association.

Validation of a personalized risk prediction model for contralateral breast cancer.

PURPOSE: Women diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors. METHODS: In this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions. RESULTS: We found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65. CONCLUSIONS: With this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PURPOSE: Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. METHODS: The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. RESULTS: In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. CONCLUSION: Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

In genomic research, it is becoming increasingly popular to perform meta-analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta-analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are mainly designed to work with full lists, whereas partial and/or top-ranked lists prevail in genomic studies. Second, the component studies are often clustered, and the existing methods fail to utilize such information. To address the above concerns, a Bayesian latent variable approach, called BiG, is proposed to formally deal with partial and top-ranked lists and incorporate the effect of clustering. Various reasonable prior specifications for variance parameters in hierarchical models are carefully studied and compared. Simulation results demonstrate the superior performance of BiG compared with other popular rank aggregation methods under various practical settings. A non-small-cell lung cancer data example is analyzed for illustration.

A model for individualized risk prediction of contralateral breast cancer.

PURPOSE: Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral BC (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task. METHODS: We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the BC risk assessment tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named CBCRisk, is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC. RESULTS: We identified eight factors to be significantly associated with CBC-age at first BC diagnosis, anti-estrogen therapy, family history of BC, high-risk pre-neoplasia status, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period. CONCLUSIONS: By providing individualized CBC risk estimates, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Semiparametric modeling and analysis of longitudinal method comparison data.

Studies comparing two or more methods of measuring a continuous variable are routinely conducted in biomedical disciplines with the primary goal of measuring agreement between the methods. Often, the data are collected by following a cohort of subjects over a period of time. This gives rise to longitudinal method comparison data where there is one observation trajectory for each method on every subject. It is not required that observations from all methods be available at each observation time. The multiple trajectories on the same subjects are dependent. We propose modeling the trajectories nonparametrically through penalized regression splines within the framework of mixed-effects models. The model also uses random effects of subjects and their interactions to capture dependence in observations from the same subjects. It additionally allows the within-subject errors of each method to be correlated. It is fit using the method of maximum likelihood. Agreement between the methods is evaluated by performing inference on measures of agreement, such as concordance correlation coefficient and total deviation index, which are functions of parameters of the assumed model. Simulations indicate that the proposed methodology performs reasonably well for 30 or more subjects. Its application is illustrated by analyzing a dataset of percentage body fat measurements. Copyright © 2017 John Wiley & Sons, Ltd.

Tolerance bands for functional data.

Often the object of inference in biomedical applications is a range that brackets a given fraction of individual observations in a population. A classical estimate of this range for univariate measurements is a "tolerance interval." This article develops its natural extension for functional measurements, a "tolerance band," and proposes a methodology for constructing its pointwise and simultaneous versions that incorporates both sparse and dense functional data. Assuming that the measurements are observed with noise, the methodology uses functional principal component analysis in a mixed model framework to represent the measurements and employs bootstrapping to approximate the tolerance factors needed for the bands. The proposed bands also account for uncertainty in the principal components decomposition. Simulations show that the methodology has, generally, acceptable performance unless the data are quite sparse and unbalanced, in which case the bands may be somewhat liberal. The methodology is illustrated using two real datasets, a sparse dataset involving CD4 cell counts and a dense dataset involving core body temperatures.

A heteroscedastic measurement error model for method comparison data with replicate measurements.

Measurement error models offer a flexible framework for modeling data collected in studies comparing methods of quantitative measurement. These models generally make two simplifying assumptions: (i) the measurements are homoscedastic, and (ii) the unobservable true values of the methods are linearly related. One or both of these assumptions may be violated in practice. In particular, error variabilities of the methods may depend on the magnitude of measurement, or the true values may be nonlinearly related. Data with these features call for a heteroscedastic measurement error model that allows nonlinear relationships in the true values. We present such a model for the case when the measurements are replicated, discuss its fitting, and explain how to evaluate similarity of measurement methods and agreement between them, which are two common goals of data analysis, under this model. Model fitting involves dealing with lack of a closed form for the likelihood function. We consider estimation methods that approximate either the likelihood or the model to yield approximate maximum likelihood estimates. The fitting methods are evaluated in a simulation study. The proposed methodology is used to analyze a cholesterol dataset.

Measuring agreement in method comparison studies with heteroscedastic measurements.

We propose a methodology for evaluation of agreement between two methods of measuring a continuous variable whose variability changes with magnitude. This problem routinely arises in method comparison studies that are common in health-related disciplines. Assuming replicated measurements, we first model the data using a heteroscedastic mixed-effects model, wherein a suitably defined true measurement serves as the variance covariate. Fitting this model poses some computational difficulties as the likelihood function is not available in a closed form. We deal with this issue by suggesting four estimation methods to obtain approximate maximum likelihood estimates. Two of these methods are based on numerical approximation of the likelihood, and the other two are based on approximation of the model. Next, we extend the existing agreement evaluation methodology designed for homoscedastic data to work under the proposed heteroscedastic model. This methodology can be used with any scalar measure of agreement. Simulations show that the suggested inference procedures generally work well for moderately large samples. They are illustrated by analyzing a data set of cholesterol measurements.

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals.

The L-statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L-statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L-statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation.

Validation of a Bayesian learning model to predict the risk for cannabis use disorder.

BACKGROUND: Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model). METHODS: We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored recalibrating the model to account for differences in population prevalence. RESULTS: The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999. CONCLUSION: The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life.

Breastfed infants metabolize perchlorate.

Bifidobacteria are the dominant intestinal bacteria in breastfed infants. It is known that they can reduce nitrate. Although no direct experiments have been conducted until now, inferred pathways for Bifidobacterium bifidum include perchlorate reduction via perchlorate reductase. We show that when commercially available strains of bifidobacteria are cultured in milk, spiked with perchlorate, perchlorate is consumed. We studied 13 breastfed infant-mother pairs who provided 43 milk samples and 39 infant urine samples, and 5 formula-fed infant-mother pairs who provided 21 formula samples and 21 infant urine samples. Using iodine as a conservative tracer, we determined the average urinary iodine (UI) to milk iodine (MI) concentration ratio to be 2.87 for the breastfed infants. For the same samples, the corresponding perchlorate concentration ratio was 1.37 (difference significant, p < 0.001), indicating that perchlorate is lost. For the formula fed infant group the same ratios were 1.20 and 1.58; the difference was not significant (p = 0.68). However, the small number of subjects in the latter group makes it more difficult to conclude definitively whether perchlorate reduction does or does not occur.

A Bayesian learning model to predict the risk for cannabis use disorder.

BACKGROUND: The prevalence of cannabis use disorder (CUD) has been increasing recently and is expected to increase further due to the rising trend of cannabis legalization. To help stem this public health concern, a model is needed that predicts for an adolescent or young adult cannabis user their personalized risk of developing CUD in adulthood. However, there exists no such model that is built using nationally representative longitudinal data. METHODS: We use a novel Bayesian learning approach and data from Add Health (n = 8712), a nationally representative longitudinal study, to build logistic regression models using four different regularization priors: lasso, ridge, horseshoe, and t. The models are compared by their prediction performance on unseen data via 5-fold-cross-validation (CV). We assess model discrimination using the area under the curve (AUC) and calibration by comparing the expected (E) and observed (O) number of CUD cases. We also externally validate the final model on independent test data from Add Health (n = 570). RESULTS: Our final model is based on lasso prior and has seven predictors: biological sex; scores on personality traits of neuroticism, openness, and conscientiousness; and measures of adverse childhood experiences, delinquency, and peer cannabis use. It has good discrimination and calibration performance as reflected by its respective AUC and E/O of 0.69 and 0.95 based on 5-fold CV and 0.71 and 1.10 on validation data. CONCLUSION: This externally validated model may help in identifying adolescent or young adult cannabis users at high risk of developing CUD in adulthood.

Primary hepatic tuberculosis presenting as fever of unknown origin diagnosed by Positron emission tomography/computed tomography scan (PET/CT).

Tuberculosis (TB), especially extrapulmonary tuberculosis (EPTB), is an important cause of fever of unknown origin (FUO) in areas endemic to TB. Liver involvement in TB in the absence of miliary TB is rare. A definitive diagnosis of primary hepatic TB can be challenging and relies on the histological and/or bacteriological findings of the liver tissue obtained by biopsy. TB should be considered in the differential diagnosis of space-occupying lesions and abscesses of the liver. In our case of a 52-year-old adult male with FUO, a positron emission tomography/computed tomography (PET/CT) scan was used, due to lack of any potential diagnostic clues, and a focal lesion was identified as a potential biopsy site.

Dog tick (Rhipecephalus) causing Lyme disease in an adult human.

Lyme disease is a tick-borne multisystem disorder transmitted by the family of Ixodes and caused by a spirochete, Borrelia. An early manifestation of the disease presents with skin lesions typically called Erythema chronicum migrans (ECM). Doxycycline should be the antibiotic of choice used for its treatment. However, we present the case of Lyme's disease in an adult male with dog tick, Rhipecephalus as a vector.

Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning.

For some, substance use during adolescence may be a stepping stone on the way to substance use disorders in adulthood. Risk prediction models may help identify adolescent users at elevated risk for hazardous substance use. This preliminary analysis used cross-sectional data (n = 270, ages 13-18) from the baseline dataset of a randomized controlled trial intervening with adolescent alcohol and/or cannabis use. Models were developed for jointly predicting quantitative scores on three measures of hazardous substance use (Rutgers Alcohol Problems Index, Adolescent Cannabis Problem Questionnaire, and Hooked on Nicotine Checklist) based on personal risk factors using two statistical and machine learning methods: multivariate covariance generalized linear models (MCGLM) and penalized multivariate regression with a lasso penalty. The predictive accuracy of a model was evaluated using root mean squared error computed via leave-one-out cross-validation. The final proposed model was an MCGLM model. It has eleven risk factors: age, early life stress, age of first tobacco use, age of first cannabis use, lifetime use of other substances, age of first use of other substances, maternal education, parental attachment, family cigarette use, family history of hazardous alcohol use, and family history of hazardous cannabis use. Different subsets of these risk factors feature in the three outcome-specific components of this joint model. The quantitative risk estimate provided by the proposed model may help identify adolescent substance users of cannabis, alcohol, and tobacco who may be at an elevated risk of developing hazardous substance use.

Krüppel-like Factor 4 activates HBG gene expression in primary erythroid cells.

The SP1/Krüppel-like Factor (SP1/KLF) family of transcription factors plays a role in diverse cellular processes, including proliferation, differentiation and control of gene transcription. The discovery of KLF1 (EKLF), a key regulator of HBB (ß-globin) gene expression, expanded our understanding of the role of KLFs in erythropoiesis. In this study, we investigated a mechanism of HBG (γ-globin) regulation by KLF4. siRNA-mediated gene silencing and enforced expression of KLF4 in K562 cells substantiated the ability of KLF4 to positively regulate endogenous HBG gene transcription. The physiological significance of this finding was confirmed in primary erythroid cells, where KLF4 knockdown at day 11 significantly attenuated HBG mRNA levels and enforced expression at day 28 stimulated the silenced HBG genes. In vitro binding characterization using the γ-CACCC and ß-CACCC probes demonstrated KLF4 preferentially binds the endogenous γ-CACCC, while CREB binding protein (CREBBP) binding was not selective. Co-immunoprecipitation studies confirmed protein-protein interaction between KLF4 and CREBBP. Furthermore, sequential chromatin immunoprecipitation assays showed co-localization of both factors in the γ-CACCC region. Subsequent luciferase reporter studies demonstrated that KLF4 trans-activated HBG promoter activity and that CREBBP enforced expression resulted in gene repression. Our data supports a model of antagonistic interaction of KLF4/CREBBP trans-factors in HBG regulation.

A preliminary risk prediction model for cannabis use disorder.

The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007-2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using leave-one-out-cross-validation to evaluate model performance. The final model is a LASSO logistic regression model consisting of the following seven risk factors: age; level of enjoyment from initial cigarette smoking; total score on Impulsive Sensation-Seeking Scale questionnaire; score on cognitive instability factor of Barratt Impulsivity Scale questionnaire; and scores on neuroticism, openness, and conscientiousness personality traits of Neuroticism, Extraversion, and Openness inventory. This model has an overall accuracy of 0.66 and the area under its receiver operating characteristic curve is 0.65. In summary, a preliminary relative risk model for predicting the quantitative risk of CUD is developed. It can be employed to identify users at high risk of CUD who may be provided with early intervention.