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N6-methyladenosine (m6A) RNA modification orchestrates cellular epitranscriptome through tuning the homeostasis of transcript stability, translation efficiency, and the transcript affinity toward RNA-binding proteins (RBPs). An aberrant m6A deposition on RNA can lead toward oncogenic expression profile (mRNA), impaired mitochondrial metabolism (mtRNA), and translational suppression (rRNA) of tumor suppressor genes. In addition, non-coding RNAs (ncRNAs), such as X-inactive specific transcript (XIST), miRNAs, and α-ketoglutarate-centric metabolic transcripts are also regulated by the m6A epitranscriptome. Notably, recent studies had uncovered a myriad of m6A-modified transcripts the center of hematopoietic stem cell (HSC) regulation, in which m6A modification act as a context dependent switch to the on and off of hematopoietic stem cell (HSC) maintenance, lineage commitment and terminal differentiation. In this review, we sequentially unfold the m6A mediated epithelial-to-hematopoietic transition in progenitor blood cell production, lymphocytic lineage expansion (T cells, B cells, NK cells, and non-NK ILCs), and the m6A crosstalk with the onco-metabolic prospects of leukemogenesis. Together, an encompassing body of evidence highlighted the emerging m6A significance in the regulation of HSC biology and leukemogenesis., (© 2024. The Author(s).)

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/m 2 , 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., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 John Wiley & Sons Ltd.)

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., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)

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., Competing Interests: Conflicts of Interest: The authors did not have any conflicts of interest.

A biomarker is a measurable indicator of the severity or presence of a disease or medical condition in biomedical or epidemiological research. Biomarkers may help in early diagnosis and prevention of diseases. Several biomarkers have been identified for many diseases such as carbohydrate antigen 19-9 for pancreatic cancer. However, biomarkers may be measured with errors due to many reasons such as specimen collection or day-to-day within-subject variability of the biomarker, among others. Measurement error in the biomarker leads to bias in the regression parameter estimation for the association of the biomarker with disease in epidemiological studies. In addition, measurement error in the biomarkers may affect standard diagnostic measures to evaluate the performance of biomarkers such as the receiver operating characteristic (ROC) curve, area under the ROC curve, sensitivity, and specificity. Measurement error may also have an effect on how to combine multiple cancer biomarkers as a composite predictor for disease diagnosis. In follow-up studies, biomarkers are often collected intermittently at examination times, which may be sparse and typically biomarkers are not observed at the event times. Joint modeling of longitudinal and time-to-event data is a valid approach to account for measurement error in the analysis of repeatedly measured biomarkers and time-to-event outcomes. In this article, we provide a literature review on existing methods to correct for estimation in regression analysis, diagnostic measures, and joint modeling of longitudinal biomarkers and survival outcomes when the biomarkers are measured with errors. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust MethodsStatistical and Graphical Methods of Data Analysis > EM AlgorithmStatistical Models > Survival Models., Competing Interests: CONFLICT OF INTEREST STATEMENT The authors have declared no conflicts of interest for this article.

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., (© 2021 The International Biometric Society.)

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., Competing Interests: Conflicts of Interest: The authors did not have any conflicts of interest.

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); P trend  = 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.

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 VO 2 max 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.

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.

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)., (© 2022. The Author(s).)

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., (© 2020 The International Biometric Society.)

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., (©2020 American Association for Cancer Research.)

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., (© 2020 John Wiley & Sons, Ltd.)

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., (© 2020 John Wiley & Sons, Ltd.)

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., (Copyright © 2019. Published by Elsevier B.V.)

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/m 2 ) 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.

We consider logistic regression with covariate measurement error. Most existing approaches require certain replicates of the error-contaminated covariates, which may not be available in the data. We propose generalized methods of moments (GMM) non-parametric correction approaches that use instrumental variables observed in a calibration subsample. The instrumental variable is related to the underlying true covariates through a general nonparametric model, and the probability of being in the calibration subsample may depend on the observed variables. We first take a simple approach adopting the inverse selection probability weighting technique using the calibration subsample. We then improve the approach based on the GMM using the whole sample. The asymptotic properties are derived and the finite sample performance is evaluated through simulation studies and an application to a real data set.

The inverse probability weighted estimator is often applied to two-phase designs and regression 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 paper, we propose a best linear inverse probability weighted estimator for two-phase designs and missing covariate regression. Our proposed estimator is the projection of the SIPW onto the orthogonal complement of the score space based on a working regression model of the observed covariate data. The efficiency gain is from the use of the association between the outcome variable and the available covariates, which is the working regression model. One advantage of the proposed estimator is that there is no need to calculate the augmented term of the augmented weighted estimator. The estimator can be applied to general missing data problems or two-phase design studies in which the second phase data are obtained in a subcohort. The method can also be applied to secondary trait case-control genetic association studies. 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 bladder cancer case-control study., (© 2019 John Wiley & Sons, Ltd.)

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.

Objective: We tested the effects of weight loss on serum estradiol, estrone, testosterone, and sex hormone-binding globulin (SHBG) in overweight/obese women 18 months after completing a year-long, 4-arm, randomized-controlled dietary weight loss and/or exercise trial., Methods: From 2005 to 2008, 439 overweight/obese, postmenopausal women (BMI >25 kg/m), 50 to 75 years, were randomized to a year-long intervention: diet (reduced calorie, 10% weight loss, N = 118), exercise (225 min/wk moderate-to-vigorous activity, N = 117), combined diet + exercise (N = 117), or control (N = 87). At 12 months, 399 women provided blood; of these, 156 returned at 30 months and gave a blood sample. Hormones and SHBG were measured by immunoassay. Changes were compared using generalized estimating equations, adjusting for confounders., Results: At 30 months, participants randomized to the diet + exercise intervention had statistically significant increases in SHBG levels versus controls (P = 0.001). There was no statistically significant change in SHBG in the exercise or diet intervention arms. Hormone levels did not vary by intervention arm from baseline to 30 months. Participants who maintained weight loss at 30 months had statistically significantly greater decreases in free estradiol and free testosterone (Ptrend = 0.02 and Ptrend = 0.04, respectively) and increases in SHBG (Ptrend < 0.0001) versus those who did not have sustained weight loss. Levels of other analytes did not vary by weight loss at 30 months., Conclusions: Sustained weight loss results in reductions in free estradiol and testosterone and increases in SHBG 18-month post-intervention.

Background: The goal of the Frequency of Eating and Satiety Hormones (FRESH) Study is to understand the relationship between eating frequency (EF) and biomarkers of appetite and disease risk. This report gives the study rationale and design., Methods: The FRESH study was conducted in n = 50 overweight and obese, but otherwise healthy, male and female adults aged 18-50 years. The protocol included four in-person clinic visits for protocol instruction, blood draws, anthropometry, and meal testing; all other activities were done at home. Participants completed two 21-day phases in random order with a two-week washout between phases. One phase was high EF (6 eating occasions/day) and the other was low EF (3 eating occasions/day). Each phase specified time of day for each eating occasion. Participants prepared their own meals throughout the study using study-provided individualized, structured meal plans ensuring that calories, macronutrients and micronutrients were identical during both study phases. Fasting blood was collected before and after each phase to test intervention effects on the biomarkers. At the end of each phase participants also completed extended appetite testing with meals prepared by the study clinic., Results: Participants were recruited using print, radio, and digital ads. 60 participants consented to enroll; 10 dropped out due to work or school scheduling conflicts and 50 (target sample size) completed the study. Compliance was assessed by completion of daily on-line meal plan checklists., Conclusions: The FRESH study will provide data on whether higher vs. lower daily EF in the context of constant energy and nutrient intake may be harmful or beneficial based on intervention effects on biomarkers of health and disease risk.

In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, we develop two non-parametric correction approaches to simultaneously correct for the informative censoring and measurement errors in the analysis of multivariate recurrent event data. A shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a non-parametric correction method using the calibration sample only is proposed. In the second approach, the information from the whole cohort is incorporated by the generalized method of moments. The proposed methods do not require the Poisson-type assumption for the multivariate recurrent event process and the distributional assumption for the frailty. Moreover, we do not need to impose any distributional assumption on the underlying covariates and measurement error. Both methods perform well, but the second approach improves efficiency. The proposed methods are applied to the Nutritional Prevention of Cancer trial to assess the effect of selenium treatment on the recurrences of basal cell carcinoma and squamous cell carcinoma., (© 2018, The International Biometric Society.)

Background: Accumulating evidence suggests that short telomere length is associated with increased overall mortality, but the relationship with cancer mortality is less clear. We examined whether telomere length (global, and chromosome arm 5p- and 13q-specific) is associated with lung cancer mortality among cases from the β-Carotene and Retinol Efficacy Trial of heavy smokers. Methods: Telomere length was measured on average 6 years before diagnosis for 788 lung cancer cases. Adjusted Cox proportional hazards models of all-cause and lung cancer-specific mortality were assessed for lung cancer overall and by histotype. Results: Short telomere length was associated with increased mortality for small cell lung cancer (SCLC), particularly stage III/IV SCLC [HR and 95% confidence interval for shortest vs. longest telomere length tertile: 3.32 (1.78-6.21)]. Associations were strongest for those randomized to the active intervention and when telomere length was measured ≤5 years before diagnosis. All-cause mortality patterns were similar. Short chromosome 5p telomere length was suggestively associated with lung cancer mortality, but there was no association with chromosome 13q telomere length. Conclusions: Our large prospective study suggests that among heavy smokers who developed lung cancer, short prediagnosis telomere length is associated with increased risk of death from SCLC. Impact: This is the first study to examine telomere length and mortality in lung cancer cases by histotype. If the association between short telomere length and SCLC mortality is replicated, elucidation of mechanisms through which telomere length influences survival for this highly aggressive cancer may inform more effective use of telomere-targeted therapeutics. Cancer Epidemiol Biomarkers Prev; 27(7); 829-37. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Background: Telomeres protect cells from genomic instability. We examined telomere length and lung cancer risk prospectively in heavy smokers., Methods: In a nested case-control study with 709 cases and 1313 controls, conditional logistic regression was used to evaluate associations between telomere length (global, chromosome 5p, and 13q) and lung cancer risk by histotype, controlling for detailed smoking history., Results: Risks of overall lung cancer and adenocarcinoma were suggestively elevated among individuals with telomere length in the longest tertile. No clear patterns were observed for other histotypes, or for chromosome 5p or 13q telomere length. Associations with adenocarcinoma were strongest among (OR, 95% CI for longest versus shortest tertile): former smokers (2.26, 1.03-4.96), individuals <65 years (2.22, 1.13-4.35), and women (2.21, 0.99-4.93)., Conclusions: Our large study of heavy smokers adds additional evidence that long telomere length prior to diagnosis is associated with risk of lung adenocarcinoma, but not other histotypes.

Many survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association 95, 1209-1219; 2006, Statistica Sinica 16, 861-881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided., (© 2017, The International Biometric Society.)

Background: We tested the effect of weight loss on circulating levels of the angiogenic factors VEGF and pigment epithelium-derived factor (PEDF) in postmenopausal overweight/obese women, 18 months after completing a year-long 4-arm randomized controlled trial of behavioral weight loss and/or exercise versus control (i.e., 30 months postrandomization). Methods: The 439 overweight/obese, postmenopausal women, ages 50 to 75 years, were randomized to: diet (goal: 10% weight loss, N = 118), exercise (225 min/wk moderate-to-vigorous activity, N = 117), diet + exercise ( N = 117), or control ( N = 87). At 12 months, 399 women gave a blood sample; 156 returned at 30 months. Biomarkers were measured by immunoassay. Changes were compared using generalized estimating equations, adjusting for baseline BMI, age, and race/ethnicity. Results: Participants randomized to diet, exercise, and diet + exercise arms had greater reductions in VEGF at 30 months (-14.1% P = 0.02; -19.7% P = 0.003; -14.5% P = 0.002, respectively) versus controls (-4.5%). There were no statistically significant changes in PEDF in any intervention arm. Participants maintaining ≥10% of baseline weight loss at 30 months had greater reductions in VEGF versus those who gained weight/had no weight change (-22.3% vs. -10.2% respectively, P = 0.002). Participants maintaining any weight loss had significantly lower levels of PEDF at 30 months versus those who gained weight/no weight change. Conclusions: Sustained weight loss via diet and/or exercise results in reductions in angiogenic factors, and can be maintained up to 30-month follow-up. Limitations include relatively small numbers, and possible bias toward more successful weight loss among women who returned at 30 months. Impact: Maintaining weight loss can achieve long-term reductions in biomarkers of angiogenesis that can persist up to 18 months after completion of a weight loss intervention. Cancer Epidemiol Biomarkers Prev; 26(12); 1788-94. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses.

Adipose tissue is involved in the etiology of postmenopausal breast cancer, possibly through increased sex steroid hormone production, inflammation, and altered adipokines. Vitamin D may affect these pathways but its effect on gene expression in different tissues has not been examined. Within a double-blind, 12-month placebo-controlled randomized trial, we compared 2000 IU/day oral vitamin D 3 supplementation ( N  = 39) vs. placebo ( N  = 40) on the expression of 5 genes in breast and adipose tissue in overweight/obese postmenopausal women (50-75 years). All participants had serum 25-hydroxyvitamin D (25(OH)D) levels ≥ 10-<32 ng/mL ("insufficient") and concurrently completed a behavioral weight loss program. Random periareolar fine needle aspiration (RPFNA) and abdominal subcutaneous adipose tissue biopsies were performed at baseline and 12 months. Changes in expression of aromatase ( CYP19A1 ), peroxisome proliferator-activated receptor gamma ( PPARG ), adiponectin ( ADIPOQ ), monocyte-chemoattractant protein 1 ( MCP-1 ), and vitamin D receptor ( VDR ) were analyzed by qRT-PCR. Compared to placebo, 2000 IU vitamin D did not show significant effects on gene expression in breast or adipose tissue. Replete women (i.e., 25(OH)D ≥ 32 ng/mL; N  = 17) showed a small decrease in MCP-1 expression compared to an increase among women who remained 'insufficient' despite supplementation ( N  = 12) (Replete:-1.6% vs. Non-replete: 61.2%, p  = 0.015) in breast, but not adipose tissue. No statistically significant differences in gene expression were detected according to degree of weight loss. Vitamin D repletion during weight loss may have different effects on gene expression in breast and adipose tissue. Further research on the localized effects of vitamin D is needed to determine its effect on breast cancer risk.

Background: Eating frequency (EF) may influence obesity-related disease risk by attenuating postprandial fluctuations in hormones involved in metabolism, appetite regulation, and inflammation., Materials/methods: This randomized crossover intervention trial tested the effects of EF on fasting plasma insulin-like growth factor-I (IGF-1) and leptin. Fifteen subjects (4 males, 11 females) completed two eucaloric intervention phases lasting 21 days each: low EF ("low-EF"; 3 eating occasions/day) and high EF ("high-EF"; 8 eating occasions/day). Subjects were free-living and consumed their own meals using individualized structured meal plans with instruction from study staff. Subjects completed fasting blood draws and anthropometry on the first and last day of each study phase. The generalized estimated equations modification of linear regression tested the intervention effect on fasting serum IGF-1 and leptin., Results: Mean (± SD) age was 28.5 ± 8.70 years, and mean (± SD) Body Mass Index was 23.3 (3.4) kg/m 2 . We found lower mean serum IGF-1 following the high-EF condition compared to the low-EF condition (P < 0.001). There was no association between EF and plasma leptin (P = 0.83)., Conclusion: These results suggest that increased EF may lower serum IGF-1, which is a hormonal biomarker linked to increased risk of breast, prostate, and colorectal cancer., Competing Interests: STATEMENT The authors declare that they have no conflicts of interest.

Reduced health-related quality of life (HRQOL), depressive symptoms and poor sleep quality are important health issues among postmenopausal women and may be associated with low vitamin D status. Overweight postmenopausal women, with serum 25-hydroxyvitamin D [25(OH)D] 10-32ng/m, were recruited in Seattle, WA (2010-2012) and randomly assigned to 12months of weight loss +2000IU oral vitamin D 3 /day or weight loss+daily placebo. The weight-loss program included a reduced-calorie diet and 225min/week of moderate-to-vigorous aerobic activity. Eight subscales of HRQOL were assessed by the MOS 36-Item Short-Form Health Survey. Depressive symptoms were assessed using the Brief Symptom Inventory-18, and sleep quality was assessed using the Pittsburg Sleep Quality Index (PSQI). Mean 12-month changes in HRQOL, depressive symptoms and sleep quality were compared between groups (intent-to-treat) using generalized estimating equations. Compared to placebo, women receiving vitamin D did not experience any significant change in depressive symptoms (p=0.78), HRQOL subscales (all p>0.05), or overall sleep quality (p=0.21). However, a greater magnitude of change in serum 25(OH)D was associated with an increased need to take medications to sleep (p trend =0.01) and overall worse sleep quality (p trend <0.01). Women who became vitamin D replete (≥32ng/mL) also showed a deterioration in total PSQI sleep quality score compared to women who remained <32ng/mL despite supplementation, even after adjusting for relevant covariates (Non-Replete: -5.7% vs. Replete: +6.2%, p<0.01). Vitamin D supplementation of 2000IU/d may result in overall worse sleep quality for postmenopausal women with low circulating vitamin D undergoing weight loss., Competing Interests: The authors have no conflicts of interest to report., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomarkers, variable selection is an important step. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We first extend the elastic net (Enet) Poisson to two penalized Poisson regression: Mnet, a combination of minimax concave and ridge penalties; and Snet, a combination of smoothly clipped absolute deviation (SCAD) and ridge penalties. Furthermore, we extend the above methods to the penalized NB regression. For the Enet, Mnet, and Snet penalties (EMSnet), we develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18, and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables., (© The Author(s) 2014.)

Biomedical researchers are often interested in estimating the effect of an environmental exposure in relation to a chronic disease endpoint. However, the exposure variable of interest may be measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies an additive measurement error model, but it may not have repeated measurements. The subset in which the surrogate variables are available is called a calibration sample. In addition to the surrogate variables that are available among the subjects in the calibration sample, we consider the situation when there is an instrumental variable available for all study subjects. An instrumental variable is correlated with the unobserved true exposure variable, and hence can be useful in the estimation of the regression coefficients. In this paper, we propose a nonparametric method for Cox regression using the observed data from the whole cohort. The nonparametric estimator is the best linear combination of a nonparametric correction estimator from the calibration sample and the difference of the naive estimators from the calibration sample and the whole cohort. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via intensive simulation studies. The methods are applied to the Nutritional Biomarkers Study of the Women's Health Initiative., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Recurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

Oxidative stress, a potential mechanism linking obesity and cancer, results from an imbalance between activation/inactivation of reactive oxygen species, byproducts of cellular metabolism. In a randomized controlled trial, we investigated effects of diet and/or exercise on biomarkers of oxidative stress. A total of 439 overweight/obese [body mass index (BMI) > 25 kg/m 2 ] postmenopausal women, ages 50 of 75 years, were randomized to 12 months of (i) reduced-calorie weight loss diet ("diet"; n = 118); (ii) moderate-to-vigorous intensity aerobic exercise ("exercise"; n = 117); (iii) combined diet and exercise intervention ("diet + exercise"; n = 117); or (iv) control (n = 87). Outcomes were circulating markers of oxidative stress, including fluorescent oxidation products (FOP), F 2 -isoprostanes, and oxidized low-density lipoprotein (LDL). On average, participants were 57.9 years, with a BMI of 30.9 kg/m 2 F 2 -isprostanes were significantly reduced in the diet (-22.7%, P = 0.0002) and diet + exercise (-23.5%, P < 0.0001) arms versus controls (-2.99%) and nonsignificantly reduced in the exercise arm (-14.5%, P = 0.01). Participants randomized to the diet and diet + exercise arms had significant increases in levels of FOP [control -5.81%; diet +14.77% (P = 0.0001); diet + exercise +17.45%, (P = 0.0001)]. In secondary analyses, increasing weight loss was statistically significantly associated with linear trends of greater reductions in oxidized LDL and in F 2 -isoprostanes and increases in FOP. Compared with controls, exercise participants whose maximal oxygen consumption increased had significant decreases in levels of F 2 -isoprostanes and in oxidized LDL and increases in FOP. Dietary weight loss, with or without exercise, significantly reduced some markers of oxidative stress in postmenopausal women. Cancer Prev Res; 9(11); 835-43. ©2016 AACR., Competing Interests: The authors declare that they have no conflicts of interest to report., (©2016 American Association for Cancer Research.)

Obese and sedentary persons have an increased risk for cancer, but underlying mechanisms are poorly understood. Angiogenesis is common to adipose tissue formation and remodeling, and to tumor vascularization. A total of 439 overweight/obese, healthy, postmenopausal women [body mass index (BMI) > 25 kg/m(2)] ages 50-75 years, recruited between 2005 and 2008 were randomized to a 4-arm 12-month randomized controlled trial, comparing a caloric restriction diet arm (goal: 10% weight loss, N = 118), aerobic exercise arm (225 minutes/week of moderate-to-vigorous activity, N = 117), a combined diet + exercise arm (N = 117), or control (N = 87) on circulating levels of angiogenic biomarkers. VEGF, plasminogen activator inhibitor-1 (PAI-1), and pigment epithelium-derived factor (PEDF) were measured by immunoassay at baseline and 12 months. Changes were compared using generalized estimating equations, adjusting for baseline BMI, age, and race/ethnicity. Participants randomized to the diet + exercise arms had statistically significantly greater reductions in PAI-1 at 12 months compared with controls (-19.3% vs. +3.48%, respectively, P < 0.0001). Participants randomized to the diet and diet + exercise arms had statistically significantly greater reductions in PEDF (-9.20%, -9.90%, respectively, both P < 0.0001) and VEGF (-8.25%, P = 0.0005; -9.98%, P < 0.0001, respectively) compared with controls. There were no differences in any of the analytes in participants randomized to the exercise arm compared with controls. Increasing weight loss was statistically significantly associated with linear trends of greater reductions in PAI-1, PEDF, and VEGF. Weight loss is significantly associated with reduced circulating VEGF, PEDF, and PAI-1, and could provide incentive for reducing weight as a cancer prevention method in overweight and obese individuals. Cancer Res; 76(14); 4226-35. ©2016 AACR., Competing Interests: The authors have no conflicts of interest to report., (©2016 American Association for Cancer Research.)

Objective: The aim of the study was to compare the effects of vitamin D3 supplementation versus placebo on serum sex hormones in postmenopausal women completing a 12-month diet + exercise weight loss program., Methods: Two hundred eighteen overweight or obese women (50-75 y) with serum 25-hydroxyvitamin D at least 10 to less than 32 ng/mL ("insufficient") were randomized to either weight loss + 2,000 IU/day oral vitamin D3, or to weight loss + daily placebo. Serum sex hormone-binding globulin, estrone, total, free, and bioavailable estradiol, and testosterone were measured by radioimmunoassay before randomization and at 12 months. Mean changes were compared between groups (intent-to-treat) using generalized estimating equations., Results: The 12-month changes in sex hormone-binding globulin, estrone, total, free, and bioavailable estradiol, and testosterone did not differ between groups (all P > 0.05). However, a greater increase in serum 25-hydroxyvitamin D was associated with a greater increase in sex hormone-binding globulin (Ptrend = 0.01), and larger decreases in free and bioavailable estradiol (Ptrend = 0.04, Ptrend = 0.03, respectively). In post-hoc analyses, we compared women randomized to vitamin D whose serum 25-hydroxyvitamin D remained insufficient (n = 38), to women who became replete (25-hydroxyvitamin D ≥32 ng/mL; n = 53). Replete women showed greater reductions in bioavailable estradiol (-1.8 vs -0.7 pg/mL), free testosterone (-0.8 vs -0.3 pg/mL), and bioavailable testosterone (-1.8 vs -0.6 ng/dL), and a greater increase in sex hormone-binding globulin (10.6 vs 4.7 nmol/L) (all P < 0.05), even after adjusting for differences in total 12-month weight loss., Conclusions: Overall, 12-month changes in sex hormone did not differ between groups. However, vitamin D repletion was associated with greater reductions in sex hormones during weight loss, with a possible dose-dependent effect. Future studies should test higher doses and target circulating 25-hydroxyvitamin D levels when measuring such effects.

In many biomedical studies, covariates of interest may be measured with errors. However, frequently in a regression analysis, the quantiles of the exposure variable are often used as the covariates in the regression analysis. Because of measurement errors in the continuous exposure variable, there could be misclassification in the quantiles for the exposure variable. Misclassification in the quantiles could lead to bias estimation in the association between the exposure variable and the outcome variable. Adjustment for misclassification will be challenging when the gold standard variables are not available. In this paper, we develop two regression calibration estimators to reduce bias in effect estimation. The first estimator is normal likelihood-based. The second estimator is linearization-based, and it provides a simple and practical correction. Finite sample performance is examined via a simulation study. We apply the methods to a four-arm randomized clinical trial that tested exercise and weight loss interventions in women aged 50-75 years., (Copyright © 2015 John Wiley & Sons, Ltd.)

Objectives: To compare the effects of 12 months of vitamin D3 supplementation with that placebo on lean mass, bone mineral density (BMD), and muscle strength in overweight or obese postmenopausal women completing a structured weight-loss program., Design: Double-blind, placebo-controlled randomized clinical trial., Setting: Fred Hutchinson Cancer Research Center, Seattle, Washington., Participants: Postmenopausal women aged 50 to 75 with a body mass index (BMI) of 25 kg/m(2) or greater and a serum 25-hydroxyvitamin D (25(OH)D) concentration between 10.0 and 32.0 ng/mL (insufficient) (N = 218)., Intervention: Oral vitamin D3 2,000 IU/d or placebo in combination with a lifestyle-based weight loss intervention consisting of a reduction of 500 kcal to 1,000 kcal per day and 225 min/wk of moderate- to vigorous-intensity aerobic exercise., Measurements: Serum 25(OH)D, body composition, and muscle strength were measured before randomization (baseline) and at 12 months. Mean changes of the groups were compared (intention to treat) using generalized estimating equations., Results: Change in 25(OH)D was significantly different between the vitamin D and placebo groups at 12 months (13.6 ng/mL vs -1.3 ng/mL, P < .001), but no differences in change in lean mass (-0.8 kg vs -1.1 kg, P = .53) or BMD of the spine (-0.01 g/cm(2) vs 0.0 g/cm(2) , P = .82) or right femoral neck (both -0.01 g/cm(2) , P = .49) were detected between the groups. Leg strength decreased in the vitamin D group but not in the placebo group (-2.6 pounds vs 1.8 pounds, P = .03). In women randomized to vitamin D, achieving repletion (25(OH)D ≥ 32 ng/mL) did not alter results., Conclusion: Vitamin D3 supplementation during weight-loss decreased leg strength but did not alter changes in lean mass or BMD in postmenopausal women with vitamin D insufficiency., (© 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.)

Under suitable assumptions and by exploiting the independence between inherited genetic susceptibility and treatment assignment, the case-only design yields efficient estimates for subgroup treatment effects and gene-treatment interaction in a Cox model. However it cannot provide estimates of the genetic main effect and baseline hazards, that are necessary to compute the absolute disease risk. For two-arm, placebo-controlled trials with rare failure time endpoints, we consider augmenting the case-only design with random samples of controls from both arms, as in the classical case-cohort sampling scheme, or with a random sample of controls from the active treatment arm only. The latter design is motivated by vaccine trials for cost-effective use of resources and specimens so that host genetics and vaccine-induced immune responses can be studied simultaneously in a bigger set of participants. We show that these designs can identify all parameters in a Cox model and that the efficient case-only estimator can be incorporated in a two-step plug-in procedure. Results in simulations and a data example suggest that incorporating case-only estimators in the classical case-cohort design improves the precision of all estimated parameters; sampling controls only in the active treatment arm attains a similar level of efficiency., (© 2015, The International Biometric Society.)

The lack of breast cancer screening in low and middle-income countries results in later stage diagnosis and worsened outcomes for women. A cluster randomized trial was performed in Bogotá, Colombia between 2008 and 2012 to evaluate effects of opportunistic breast cancer screening. Thirteen clinics were randomized to an intervention arm and 13 to a control arm. Physicians in intervention clinics were instructed to perform clinical breast examination on all women aged 50-69 years attending clinics for non-breast health issues, and then refer them for mammographic screening. Physicians in control clinics were not explicitly instructed to perform breast screening or mammography referrals, but could do so if they thought it indicated ("usual care"). Women were followed for 2-years postrandomization. 7,436 women were enrolled and 7,419 (99.8%) screened in intervention clinics, versus 8,419 enrolled and 1,108 (13.1%) screened in control clinics. Incidence ratios (IR) of early, advanced and all breast cancers were 2.9 (95% CI 1.1-9.2), 1.0 (0.3-3.5) and 1.9 (0.9-4.1) in the first (screening) year of the trial, and the cumulative IR for all breast cancers converged to 1.4 (0.7-2.8) by the end of follow-up (Year 2). Eighteen (69.2%) of 26 women with early stage disease had breast conservation surgery (BCS) versus 6 (42.5%) of 14 women with late-stage disease (p = 0.02). Fifteen (68.2%) of 22 women with breast cancer in the intervention group had BCS versus nine (50.0%) of 18 women in the control group (p = 0.34). Well-designed opportunistic clinic-based breast cancer screening programs may be useful for early breast cancer detection in LMICs., (© 2015 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC.)

Background: Mexican immigrants are disproportionally affected by diet-related risk of metabolic dysfunction. Whether adhering to a traditional Mexican diet or adopting a US diet contributes to metabolic changes associated with future risk of type 2 diabetes and other chronic diseases has not been investigated., Objective: The purpose of this study was to test in a randomized crossover feeding trial the metabolic responses to a Mexican diet compared with a commonly consumed US diet., Design: First- and second-generation healthy women of Mexican descent (n = 53) were randomly assigned in a crossover design to consume a Mexican or US diet for 24 d each, separated by a 28-d washout period. Diets were eucaloric and similar in macronutrient composition. The metabolic responses to diets were assessed by measuring fasting serum concentrations of glucose, insulin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), adiponectin, C-reactive protein (CRP), and interleukin 6 (IL-6), as well as the homeostasis model assessment of insulin resistance (HOMA-IR) at the beginning and end of each period. Linear mixed models tested the intervention effect on the biomarkers, while adjusting for diet sequence, feeding period, baseline and washout biomarker concentrations, age, acculturation, and BMI., Results: Compared with the US diet, the Mexican diet reduced insulin by 14% [geometric means (95% CIs): 9.3 (8.3, 10.3) compared with 8.0 (7.2, 8.9) μU/mL; P = 0.02], HOMA-IR by 15% [2.0 (1.8, 2.3) compared with 1.7 (1.6, 2.0); P = 0.02], and IGFBP-3 by 6% (mean ± SEM: 2420 ± 29 compared with 2299 ± 29 ng/mL; P < 0.01) and tended to reduce circulating concentrations of IGF-1 by 4% (149 ± 2.6 compared with 144 ± 2.5 ng/mL; P = 0.06). There was no significant intervention effect on serum concentrations of glucose, adiponectin, CRP, or IL-6 in the US compared with the Mexican diet., Conclusion: Compared with the commonly consumed US diet, the traditional Mexican diet modestly improved insulin sensitivity under conditions of weight stability in healthy women of Mexican descent, while having no impact on biomarkers of inflammation. This trial was registered at clinicaltrials.gov as NCT01369173., (© 2016 American Society for Nutrition.)

Background: Consumption of small, frequent meals is suggested as an effective approach to control appetite and food intake and might be a strategy for weight loss or healthy weight maintenance. Despite much speculation on the topic, scientific evidence is limited to support such a relation in the absence of changes to diet composition., Objective: We examined the effects of high compared with low eating frequency (EF) on self-reported appetite as a secondary outcome in a controlled trial., Methods: We conducted a randomized, crossover intervention trial in 12 participants (4 men, 8 women) who completed 2 isocaloric 3-wk intervention phases of low EF (3 eating occasions/d) compared with high EF (8 eating occasions/d). On the last morning of each study phase, participants completed a 4-h appetite testing session. During the appetite testing session, participants completing the low EF phase consumed a meal at 0800. Participants completing the high EF intervention consumed the same meal spread evenly over 2 eating occasions at 0800 and 1030. Standardized ratings of hunger, desire to eat, fullness, thirst, and nausea were completed every 30 min with the use of paper-and-pencil semianchored 100-mm visual analog scales. A composite appetite score was calculated as the mean of hunger, desire to eat, and the inverse of fullness (calculated as 100-fullness rating). Linear regression analysis compared ratings between low EF and high EF conditions., Results: The mean composite appetite score was higher in the high EF condition for the total testing period (baseline through 1200) (P < 0.05) and for the time period from baseline through 1030 (P < 0.001)., Conclusion: The results from this study in 12 healthy adults do not support the popularized notion that small, frequent meals help to decrease overall appetite. This trial was registered at clinicaltrials.gov as NCT02548026., (© 2016 American Society for Nutrition.)

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, but also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using the open-source R package mpath., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Obesity and vitamin D deficiency are associated with risk for several cancers, possibly through inflammation and adipokine-related pathways. Two hundred and eighteen postmenopausal women with BMI > 25 kg/m(2) and low serum 25-hydroxyvitamin D (25(OH)D; ≥10-<32 ng/mL), were randomized to 12 months of either (i) weight-loss intervention + 2000 IU/day oral vitamin D3 or (ii) weight-loss intervention + daily placebo. Serum adiponectin, leptin, TNFα, IL6, IL1β, IL8, and IL10, were measured by immunoassay, and a composite inflammatory biomarker score calculated. Using generalized estimating equations, mean changes in outcomes were compared between arms (intent-to-treat), adjusted for possible confounders. Analyses were also stratified by weight-loss (gained/no weight-loss; <5%; 5% to 10%; ≥10%). At 12 months, there were no significant differences in analyte changes between arms. In stratified analyses, participants randomized to vitamin D3 who lost 5% to 10% of baseline weight, versus participants who gained weight/had no weight-loss, had significantly greater decreases in levels of IL6 compared with those randomized to placebo: absolute change -0.75 pg/mL (-17.2%), placebo versus -1.77 pg/mL (-37.3%), vitamin D, P = 0.004. Similar but attenuated results were observed for participants who lost ≥10% of baseline weight: -0.41 pg/mL (-13.6%), placebo versus -0.67 pg/mL (-17.3%), vitamin D, P = 0.02. Effects of vitamin D3 supplementation on levels of IL1β were inconsistent when stratified by weight loss. There were no intervention effects on IL10, TNFα, IL8, the composite score, adiponectin, or leptin, when stratified by weight-loss. In conclusion, vitamin D3 supplementation in combination with weight-loss of at least 5% of baseline weight was associated with significant reductions in levels of IL6., (©2015 American Association for Cancer Research.)

Introduction: Recent colorectal cancer screening studies focus on optimizing adherence. This study evaluated the cost effectiveness of interventions using electronic health records (EHRs); automated mailings; and stepped support increases to improve 2-year colorectal cancer screening adherence., Methods: Analyses were based on a parallel-design, randomized trial in which three stepped interventions (EHR-linked mailings ["automated"]; automated plus telephone assistance ["assisted"]; or automated and assisted plus nurse navigation to testing completion or refusal [navigated"]) were compared to usual care. Data were from August 2008 to November 2011, with analyses performed during 2012-2013. Implementation resources were micro-costed; research and registry development costs were excluded. Incremental cost-effectiveness ratios (ICERs) were based on number of participants current for screening per guidelines over 2 years. Bootstrapping examined robustness of results., Results: Intervention delivery cost per participant current for screening ranged from $21 (automated) to $27 (navigated). Inclusion of induced testing costs (e.g., screening colonoscopy) lowered expenditures for automated (ICER=-$159) and assisted (ICER=-$36) relative to usual care over 2 years. Savings arose from increased fecal occult blood testing, substituting for more expensive colonoscopies in usual care. Results were broadly consistent across demographic subgroups. More intensive interventions were consistently likely to be cost effective relative to less intensive interventions, with willingness to pay values of $600-$1,200 for an additional person current for screening yielding ≥80% probability of cost effectiveness., Conclusions: Two-year cost effectiveness of a stepped approach to colorectal cancer screening promotion based on EHR data is indicated, but longer-term cost effectiveness requires further study., (Copyright © 2015 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)