Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates.

Epidemiological studies often encounter a challenge due to exposure measurement error when estimating an exposure-disease association. A surrogate variable may be available for the true unobserved exposure variable. However, zero-inflated data are encountered frequently in the surrogate variables. For example, many nutrient or physical activity measures may have a zero value (or a low detectable value) among a group of individuals. In this paper, we investigate regression analysis when the observed surrogates may have zero values among some individuals of the whole study cohort. A naive regression calibration without taking into account a probability mass of the surrogate variable at 0 (or a low detectable value) will be biased. We developed a regression calibration estimator which typically can have smaller biases than the naive regression calibration estimator. We propose an expected estimating equation estimator which is consistent under the zero-inflated surrogate regression model. Extensive simulations show that the proposed estimator performs well in terms of bias correction. These methods are applied to a physical activity intervention study.

No Effect of High Eating Frequency Compared with Low Eating Frequency on Appetite and Inflammation Biomarkers: Results from a Randomized Crossover Clinical Trial.

BACKGROUND: Eating frequency (EF) focuses on the total number of eating occasions per day and may influence metabolic health. OBJECTIVES: We sought to examine the effect of high compared with low EF on appetite regulation and inflammatory biomarkers among healthy adults. METHODS: Data are from a randomized, crossover trial (the Frequency of Eating and Satiety Hormones study). Participants (n = 50) completed 2 isocaloric 21-d study periods of low EF (3 eating occasions/d) and high EF (6 eating occasions/d) in random order with a 14-d washout period in between. Participants were free-living and consumed their own food, using study-directed, structured meal plans with identical foods and total energy in both study periods. On days 1 and 21 of each EF period, fasting blood was collected during in-person clinic visits to assess plasma concentrations of ghrelin, leptin, adiponectin, and high-sensitivity C-reactive protein (hs-CRP). Linear mixed models with EF, diet sequence, and period as fixed effects and participant as random effect were used to estimate the intervention effect. Interaction effects between EF and body fat percentage were examined. RESULTS: Among the 50 participants who completed the trial, 39 (78%) were women, 30 (60%) were Non-Hispanic White, and 40 (80%) had a body mass index of <25 kg/m2, and the mean age was 32.1 y. The differences between high and low EF in fasting ghrelin (geometric mean difference: 17.76 ng/mL; P = 0.60), leptin (geometric mean difference: 2.09 ng/mL; P = 0.14), adiponectin (geometric mean difference: 381.7 ng/mL; P = 0.32), and hs-CRP (geometric mean difference: -0.018 mg/dL; P = 0.08) were not statistically significant. No significant interaction was observed between EF and body fat percentage on appetite regulation and inflammatory biomarkers. CONCLUSIONS: No differences was observed in fasting ghrelin, leptin, adiponectin, and hs-CRP comparing high and low EF. Future studies are needed to understand the physiology of EF and appetite as they relate to metabolic health. This trial was registered at clinicaltrials.gov as NCT02392897.

Robust best linear weighted estimator with missing covariates in survival analysis.

Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators, but in general are more robust against model misspecification. In this article, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer-specific mortality.

A Flexible Method for Diagnostic Accuracy with Biomarker Measurement Error.

Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, among others. Ignoring measurement error can cause biased estimation of a diagnostic accuracy measure, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Existing assays available are either research grade or clinical grade. Research assays are cost effective, often multiplex, but they may be associated with moderate measurement errors leading to poorer diagnostic performance. In comparison, clinical assays may provide better diagnostic ability, but with higher cost since they are usually developed by industry. Correction for attenuation methods are often valid when biomarkers are from a normal distribution, but may be biased with skewed biomarkers. In this paper, we develop a flexible method based on skew-normal biomarker distributions to correct for bias in estimating diagnostic performance measures including AUC, sensitivity, and specificity. Finite sample performance of the proposed method is examined via extensive simulation studies. The methods are applied to a pancreatic cancer biomarker study.

Spondyloarthritis and nonbacterial thrombotic endocarditis as paraneoplastic manifestations in treatment-naive Burkitt lymphoma.

Non-radiographic axial spondyloarthropathy (nr-axSpA) is a clinical diagnosis of symptoms matching inflammatory back pain criteria without radiological lesions at the sacroiliac joint. The frequency of an early nr-axSpA-like presentation in lymphoma patients has not been clarified. Here we report a woman in her 20s with a fever and musculoskeletal discomfort. Detailed investigations revealed that she was suffering from Burkitt lymphoma in which nr-axSpA-like symptoms were a musculoskeletal manifestation of the disease, irrelevant to the anti-neoplastic treatment.

A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error-contaminated continuous time-dependent exposure.

We consider the proportional hazards model in which the covariates include the discretized categories of a continuous time-dependent exposure variable measured with error. Naively ignoring the measurement error in the analysis may cause biased estimation and erroneous inference. Although various approaches have been proposed to deal with measurement error when the hazard depends linearly on the time-dependent variable, it has not yet been investigated how to correct when the hazard depends on the discretized categories of the time-dependent variable. To fill this gap in the literature, we propose a smoothed corrected score approach based on approximation of the discretized categories after smoothing the indicator function. The consistency and asymptotic normality of the proposed estimator are established. The observation times of the time-dependent variable are allowed to be informative. For comparison, we also extend to this setting two approximate approaches, the regression calibration and the risk-set regression calibration. The methods are assessed by simulation studies and by application to data from an HIV clinical trial.

Weight loss reduces circulating micro-RNA related to obesity and breast cancer in postmenopausal women.

Postmenopausal women with overweight or obesity have an increased risk of developing breast cancer but many of the mechanisms underlying this association remain to be elucidated. MicroRNAs (miRNAs), short non-coding single-stranded RNAs, regulate many physiological processes by controlling post-transcriptional regulation of mRNA. We measured circulating miRNA from 192 overweight/obese postmenopausal women (50-75 years) who were part of a randomized controlled trial, comparing independent and combined effects of a 12-month reduced-calorie weight-loss diet and exercise programme, versus control. RNA was extracted from stored plasma samples, and 23 a priori selected miRNA targets related to aetiology of breast cancer or obesity were measured using NanoString nCounter miRNA Expression assays. Changes from baseline to 12-months between controls and women in the diet/exercise weight loss arms were analysed using generalized estimating equations modification of linear regression, adjusted for confounders. We next examined changes in levels of circulating miRNA by amount of weight loss (0-10% versus ≥10%). Participants randomized to weight-loss interventions had statistically significantly greater reductions in miR-122 (-7.25%), compared to controls (+ 33.5%, P = 0.009), and miR-122 levels were statistically significantly correlated with weight loss (rho = 0.24; P = 0.001) Increasing weight loss was associated with greater reductions in miR-122 vs. controls (-11.7% (≥10% weight loss); +2.0% (0-10% weight loss) +33.5% (controls); Ptrend = 0.006), though this was not significant after correction for multiple testing (P = 0.05/23) Our study supports the effect of weight loss on regulation of miRNA.

Population Size Estimation using Zero-truncated Poisson Regression with Measurement Error.

Population size estimation is an important research field in biological sciences. In practice, covariates are often measured upon capture on individuals sampled from the population. However, some biological measurements, such as body weight may vary over time within a subject's capture history. This can be treated as a population size estimation problem in the presence of covariate measurement error. We show that if the unobserved true covariate and measurement error are both normally distributed, then a naïve estimator without taking into account measurement error will under-estimate the population size. We then develop new methods to correct for the effect of measurement errors. In particular, we present a conditional score and a nonparametric corrected score approach that are both consistent for population size estimation. Importantly, the proposed approaches do not require the distribution assumption on the true covariates, furthermore the latter does not require normality assumptions on the measurement errors. This is highly relevant in biological applications, as the distribution of covariates is often non-normal or unknown. We investigate finite sample performance of the new estimators via extensive simulated studies. The methods are applied to real data from a capture-recapture study.

Two meta-analyses of the association between atopic diseases and core symptoms of attention deficit hyperactivity disorder.

Studies in the field of neuroscience and psychology have hypothesized that a causal association exists between atopic diseases and attention-deficit/hyperactivity disorder (ADHD). Previous systematic reviews and meta-analyses have reported a higher risk of ADHD in children with atopic diseases; however, the relationship between ADHD symptoms and atopic diseases remains unclear. We systematically reviewed observational cross-sectional and longitudinal studies to investigate the relationship between atopic diseases and ADHD symptom severity (hyperactivity/impulsivity and inattention). The majority of studies showed a statistically significant association between atopic diseases and both ADHD symptoms, with substantial heterogeneity in the outcome of hyperactivity/impulsivity. Remarkably decreased heterogeneity and statistical significance were observed in the second meta-analysis of ADHD-related behavior symptoms in atopic patients without ADHD. Our study indicated that atopic diseases not only associated with ADHD but also ADHD symptoms severity. This association was even observed in children with subthreshold ADHD, indicating that atopic diseases may play a role in the spectrum of ADHD symptom severity. Trial registration: This study was registered on PROSPERO (registration ID: CRD42020213219).

Exercise effects on DNA methylation in EVL, CDKN2A (p14, ARF), and ESR1 in colon tissue from healthy men and women.

Physical activity reduces risk of colon cancer by 20-30%. Aberrant methylation patterns are common epigenetic alterations in colorectal adenomas, and cancers and play a role in cancer initiation and progression. Alterations identified in normal colon tissue represent apotential 'field cancerization' process, where normal colon is primed for carcinogenesis. Here, we investigate methylation patterns in three genes -Ena/VASP-like (EVL), (CDKN2A (p14, ARF)), and Oestrogen Receptor-1 (ESR1)- in normal colon tissue collected at baseline and 12 months from 202 sedentary men and women, 40-75 years, enrolled in a randomized controlled trial testing an exercise intervention vs. control (http://clinicaltrials.gov/show/NCT00668161). Participants were randomized to moderate-to-vigorous intensity exercise, 60 minutes/day, 6 days/week for 12 months, or usual lifestyle. Sigmoid colon biopsies were obtained at baseline and 12-months, DNA extracted, and bisulphite converted. Droplet digital methylation-specific PCR was performed for EVL, p14ARF, and ESR1. Generalized estimating equations modification of linear regression was used to model relationships between intervention effects and gene methylation levels, adjusting for possible confounders.There were no statistically significant differences between methylation patterns at 12-months between exercisers and controls. ESR1 methylation patterns differed by sex: women -10.58% (exercisers) +11.10% (controls); men +5.54% (exercisers), -8.16% (controls) (P=0.05), adjusting for BMI and age. There were no statistically significant changes in methylation patterns in any gene stratified by change in VO2max or minutes/week of exercise.While no statistically significant differences were found in gene methylation patterns comparing exercises vs. controls, 12-month exercise effects on ESR1 methylation differed by sex, warranting further study.

Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.

Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. Regression calibration (RC) is a common approach to correct for bias in regression analysis with covariate measurement error. In survival analysis with covariate measurement error, it is well known that the RC estimator may be biased when the hazard is an exponential function of the covariates. In the paper, we investigate the RC estimator with general hazard functions, including exponential and linear functions of the covariates. When the hazard is a linear function of the covariates, we show that a risk set regression calibration (RRC) is consistent and robust to a working model for the calibration function. Under exponential hazard models, there is a trade-off between bias and efficiency when comparing RC and RRC. However, one surprising finding is that the trade-off between bias and efficiency in measurement error research is not seen under linear hazard when the unobserved covariate is from a uniform or normal distribution. Under this situation, the RRC estimator is in general slightly better than the RC estimator in terms of both bias and efficiency. The methods are applied to the Nutritional Biomarkers Study of the Women's Health Initiative.

Changes in Dietary Inflammatory Index Patterns with Weight Loss in Women: A Randomized Controlled Trial.

Dietary composition can influence systemic inflammation; higher levels of circulating inflammatory biomarkers are associated with increased risk of breast and other cancers. A total of 438 overweight/obese, healthy, postmenopausal women were randomized to a caloric-restriction diet (goal: 10% weight-loss), aerobic-exercise (225 min/week moderate-to-vigorous activity), combined diet+exercise, or control. Dietary inflammatory index (DII) and energy-adjusted (E-DII) scores were derived from food frequency questionnaires (FFQ) and could be calculated for 365 participants with complete FFQs at baseline and 12 months. Changes from baseline to 12 months in E-DII scores in the intervention arms versus controls were analyzed using generalized estimating equations, adjusted for confounders. We examined associations between changes in previously measured biomarkers and E-DII at 12 months. Participants randomized to diet and diet+exercise arms had greater reductions in E-DII (-104.4% and -84.4%), versus controls (-34.8%, both P < 0.001). Weight change had a more marked effect than E-DII change on biomarkers at 12-months; associations between E-DII and biomarker changes were reduced after adjustment by weight change. Changes in E-DII at 12 months, adjusted for weight change, were negatively associated with changes in ghrelin [r = -0.19; P = 0.05 (diet), r = -0.29; P = 0.02 (diet+exercise)], and positively with VEGF [r = 0.22; P = 0.03 (diet+exercise)], and red blood cell counts [r = 0.30; P = 0.004 (exercise)]. C-reactive protein (CRP) and IL6 levels were not associated with E-DII changes at 12 months. In conclusion, a behavior change of low-calorie, low-fat diet significantly reduces dietary inflammatory potential, modulating biomarkers that are associated with tumorigenesis, such as VEGF, but not CRP or IL6. PREVENTION RELEVANCE: Diets high in saturated fats and low in fruit and vegetable intake are associated with increased inflammation, which increases cancer risk. This study showed that changes in diet quality had effects on factors associated with cancer; however, the majority of beneficial effects were associated with weight loss rather than diet quality.

Multinomial logistic regression with missing outcome data: An application to cancer subtypes.

Many diseases such as cancer and heart diseases are heterogeneous and it is of great interest to study the disease risk specific to the subtypes in relation to genetic and environmental risk factors. However, due to logistic and cost reasons, the subtype information for the disease is missing for some subjects. In this article, we investigate methods for multinomial logistic regression with missing outcome data, including a bootstrap hot deck multiple imputation (BHMI), simple inverse probability weighted (SIPW), augmented inverse probability weighted (AIPW), and expected estimating equation (EEE) estimators. These methods are important approaches for missing data regression. The BHMI modifies the standard hot deck multiple imputation method such that it can provide valid confidence interval estimation. Under the situation when the covariates are discrete, the SIPW, AIPW, and EEE estimators are numerically identical. When the covariates are continuous, nonparametric smoothers can be applied to estimate the selection probabilities and the estimating scores. These methods perform similarly. Extensive simulations show that all of these methods yield unbiased estimators while the complete-case (CC) analysis can be biased if the missingness depends on the observed data. Our simulations also demonstrate that these methods can gain substantial efficiency compared with the CC analysis. The methods are applied to a colorectal cancer study in which cancer subtype data are missing among some study individuals.

Methods for generalized change-point models: with applications to human immunodeficiency virus surveillance and diabetes data.

In many epidemiological and biomedical studies, the association between a response variable and some covariates of interest may change at one or several thresholds of the covariates. Change-point models are suitable for investigating the relationship between the response and covariates in such situations. We present change-point models, with at least one unknown change-point occurring with respect to some covariates of a generalized linear model for independent or correlated data. We develop methods for the estimation of the model parameters and investigate their finite-sample performances in simulations. We apply the proposed methods to examine the trends in the reported estimates of the annual percentage of new human immunodeficiency virus (HIV) diagnoses linked to HIV-related medical care within 3 months after diagnosis using HIV surveillance data from the HIV prevention trial network 065 study. We also apply our methods to a dataset from the Pima Indian diabetes study to examine the effects of age and body mass index on the risk of being diagnosed with type 2 diabetes.

Effects of influenza vaccine and sun exposure time against laboratory-confirmed influenza hospitalizations among young children during the 2012-13 to 2015-16 influenza seasons.

BACKGROUND: Influenza is a major cause of acute respiratory infection burden worldwide, leading to many hospitalizations. An annual influenza vaccine is believed to be the best way to prevent influenza-related illnesses. We focused on the efficacies of other possible preventive measures such as increasing sun exposure time and dietary supplements to prevent these illnesses. METHODS: We conducted a matched-pair case-control study along with the Taiwan Pediatric Infectious Disease Alliance. We included influenza-related hospitalized patients with age ranging from 6 months to 5 years during the 2012-2013, 2013-2014, 2014-2015, and 2015-2016 influenza seasons. The controls were comparable to cases in age, sex, and residential area and had no influenza-related hospitalization records in the same season. We extracted data from vaccination histories and got the patients' guardians to complete questionnaires. Data were analyzed using conditional logistic regression. RESULTS: We enrolled 1514 children (421 influenza-infected cases and 1093 controls) in the study. We found seasonal influenza vaccination to be an independent protective factor against hospitalizations owing to influenza [p < 0.01; odds ratio (OR), 0.427; 95% confidence interval (CI), 0.306-0.594]. Children with mean sun exposure time of >7 h/week had a significantly lower risk of influenza-related hospitalizations than those with the mean sun exposure time of ≤7 h/week (p < 0.05; OR, 0.667; 95% CI, 0.491-0.906). CONCLUSIONS: Seasonal influenza vaccination effectively prevents influenza-related hospitalizations in children aged ≤5 years. Besides, >7 h of sun exposure/week may also be associated with lower risk of influenza-related hospitalizations in children.

Eating behaviors and weight loss outcomes in a 12-month randomized trial of diet and/or exercise intervention in postmenopausal women.

BACKGROUND: Certain eating behaviors are common among women with obesity. Whether these behaviors influence outcomes in weight loss programs, and whether such programs affect eating behaviors, is unclear. METHODS: Our aim was to examine the effect of baseline eating behaviors on intervention adherence and weight among postmenopausal women with overweight or obesity, and to assess intervention effects on eating behaviors. Four hundred and 39 women (BMI ≥25 kg/m2) were randomized to 12 months of: i) dietary weight loss with a 10% weight loss goal ('diet'; n = 118); ii) moderate-to-vigorous intensity aerobic exercise for 225 mins/week ('exercise'; n = 117); iii) combined dietary weight loss and exercise ('diet + exercise'; n = 117); or iv) no-lifestyle change control (n = 87). At baseline and 12 months, restrained eating, uncontrolled eating, emotional eating and binge eating were measured by questionnaire; weight and body composition were assessed. The mean change in eating behavior scores and weight between baseline and 12 months in the diet, exercise, and diet + exercise arms were each compared to controls using the generalized estimating equation (GEE) modification of linear regression adjusted for age, baseline BMI, and race/ethnicity. RESULTS: Baseline restrained eating was positively associated with change in total calories and calories from fat during the dietary intervention but not with other measures of adherence. Higher baseline restrained eating was associated with greater 12-month reductions in weight, waist circumference, body fat and lean mass. Women randomized to dietary intervention had significant reductions in binge eating (- 23.7%, p = 0.005 vs. control), uncontrolled eating (- 24.3%, p < 0.001 vs. control), and emotional eating (- 31.7%, p < 0.001 vs. control) scores, and a significant increase in restrained eating (+ 60.6%, p < 0.001 vs. control); women randomized to diet + exercise reported less uncontrolled eating (- 26.0%, p < 0.001 vs. control) and emotional eating (- 22.0%, p = 0.004 vs. control), and increased restrained eating (+ 41.4%, p < 0.001 vs. control). Women randomized to exercise alone had no significant change in eating behavior scores compared to controls. CONCLUSIONS: A dietary weight loss intervention helped women modify eating behaviors. Future research should investigate optimal behavioral weight loss interventions for women with both disordered eating and obesity. TRIAL REGISTRATION: NCT00470119 (https://clinicaltrials.gov). Retrospectively registered May 7, 2007.

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data.

Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.