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Background: Radiocephalic arteriovenous fistula (RCAVF) is the best access modality to be considered initially when planning arteriovenous fistula (AVF) for maintenance hemodialysis. Considering the higher incidence of RCAVF failed maturation (M), it is important to perform proper preoperative evaluation and identification of high-risk patients. There has been no study on the influence of preoperative cardiac function on the M and patency of AVFs. The purpose of this investigation is to determine whether preoperative cardiac index (CI) is a predictor of M and primary patency of RCAVF. Method: A total of 365 end-stage renal disease patients undergoing RCAVF surgery were consecutively enrolled with a median follow-up time of 20 months in this prospective cohort study. Demographics, vascular diameters measured by duplex ultrasound examination, and CI measured by echocardiography, were analyzed for effect on RCAVF primary functional M and primary patency. Result: Patients in the group achieving primary RCAVF functional M had a significantly larger mean CI than the group with early RCAVF failure (2.93 ± 0.77 vs. 3.57 ± 0.76 L/min/m2, p < 0.001). The receiver operating characteristic curve was plotted and demonstrated that preoperative vein diameter and CI can predict failure of RCAVF M. The AUC of CI was higher (0.745 vs. 0.666). Multivariate regression analysis, adjusted for age, sex, diabetes, preoperative dialysis status and vessel diameters, showed that decreased CI remained associated with increased risk of failure of M (FM) and worse primary unassisted patency. The Kaplan-Meier survival analysis suggested that patients with CI 2 had a worse primary unassisted patency rate at all time points compared with patients with CI ≥3 L/min/m2. Conclusion: This study demonstrated that preoperative CI was associated with RCAVF M and long-term patency. A decreased CI may be a possible predictor of an increased risk of FM and a shorter primary patency time.

A probability method is developed to decide when to initiate cancer screening for asymptomatic individuals. The probability of incidence is a function of screening sensitivity, time duration in the disease-free state and sojourn time in the preclinical state; and it is monotonically increasing as time increases, given a person's current age. So a unique solution of the first screening time can be found by limiting this probability to a small value, such as 10% or 20%. That is, with 90% or 80% probability, one will not be a clinical incident case before the first exam. After this age is found, we can further estimate the lead time distribution and probability of over-diagnosis if one would be diagnosed with cancer at the first exam. Simulations were carried out under different scenarios; and the method was applied to two heavy smoker cohorts in the National Lung Screening Trial using low-dose computerized tomography. The method is applicable to other kinds of cancer screening. The predictive information can be used by physicians or individuals at risk to make informed decisions on when to initiate screening.

Aims: The aim of this study is to provide statistical inference of the sojourn time and the transition probability from the disease free to the preclinical state of lung cancer for male and female smokers using lung cancer data from the National Lung Screening Trial (NLST). Materials and Methods: We applied a likelihood function to the lung cancer data, to obtain Bayesian inference of the transition probability and the sojourn time distribution. A log-normal distribution was used for the transition probability density function multiplied by 30%, and a Weibull distribution was used to model the sojourn time in the preclinical state. Results: The estimate of screening sensitivity is 0.61 for males and 0.62 for females. Early transition happened before age 50 and lasted until after age 90. The transition probability from the disease free to the preclinical state has a single maximum at around age 73 for males and 72 for females. For male, the Bayesian posterior mean, and median sojourn time are 1.33 and 1.27 years, respectively. For female, the corresponding posterior mean, and median sojourn time are 1.23 and 1.21 years, respectively. Conclusion: Our estimation showed that male smokers are more vulnerable to lung cancer, because they have a higher transition probability density than the same aged female smokers. The female smokers have a slightly shorter mean sojourn time than the male, meaning that they are quicker to develop clinical symptom of lung cancer.

Traditional phase III clinical trials are powered to detect an overall treatment effect. However, it has increasingly been shown that many treatments are effective only for a subset of a population. The adaptive signature design uses genomic/proteomic information to prospectively predict a subset of patients more sensitive to treatment. Tests for overall treatment effect and for treatment effect in the predicted subset are conducted. In this work properties of the adaptive signature design are investigated through simulation. It was found that models which excluded expression main effect terms had higher empirical power than models which included them.

We have been working in the area of cancer screening modeling for many years. A well-known and frequently used model in cancer screening is the progressive three-state model [1], where all cancer patients are assumed to go through three states: the disease-free state when one is cancer-free or the cancer is in an early stage that no technology can find; the preclinical state when one without symptom but cancer could be detected by screening, and the clinical state when cancer related symptoms show up. There are three key parameters in the model: a) the screening sensitivity, the probability of a positive screening result given that one is in the preclinical state; b) the distribution of sojourn time, which measures the time duration in the preclinical state; and c) the transition density, which measures the time duration in the disease-free state, or the onset age of the preclinical state. These three parameters are called the key parameters since they determines the screening processes and all other terms, for example, lead time (diagnosis time advanced by screening), probability of over-diagnosis, etc., are functions of these three. Therefore accurate estimation of these three key parameters is critical and lays a foundation for all other estimations.

High throughput RNA sequencing (RNA-seq) technology is increasingly used in disease-related biomarker studies. A negative binomial distribution has become the popular choice for modeling read counts of genes in RNA-seq data due to over-dispersed read counts. In this study, we propose two explicit sample size calculation methods for RNA-seq data using a negative binomial regression model. To derive these new sample size formulas, the common dispersion parameter and the size factor as an offset via a natural logarithm link function are incorporated. A two-sided Wald test statistic derived from the coefficient parameter is used for testing a single gene at a nominal significance level 0.05 and multiple genes at a false discovery rate 0.05. The variance for the Wald test is computed from the variance-covariance matrix with the parameters estimated from the maximum likelihood estimates under the unrestricted and constrained scenarios. The performance and a side-by-side comparison of our new formulas with three existing methods with a Wald test, a likelihood ratio test or an exact test are evaluated via simulation studies. Since other methods are much computationally extensive, we recommend our M1 method for quick and direct estimation of sample sizes in an experimental design. Finally, we illustrate sample sizes estimation using an existing breast cancer RNA-seq data.

It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classifi- cation methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. More recent methods have built-in dimension reduction methods for high throughput data. Pre-validation is one method of assessing the added value of high-throughput data to clinical covariates. The way in which treatment interactions are incorporated is important if the goal is to predict a subset of patients which respond differently to treatment. For nonparametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions. It is hoped that this study will stimulate more development of nonparametric methods to predict subsets of patients responding differently to treatment.

Based on recent reports from the National Lung Screening Trial (NLST), smokers who were screened by low-dose computer tomography (LDCT) had a 20% lower chance of dying from lung cancer than those screened by chest X-rays. However, due to the complexities of lead time bias and over diagnosis, no formal test has been shown to reduce lung cancer mortality. To correctly evaluate survival benefit due to early detection, it is critical to estimate lead time, the length of time that detection of a disease is advanced by screening. We applied a recently developed probability method to estimate lead time, using the LDCT data from NLST, where human lifetime was treated as random and derived from the actuarial life table of the US Social Security Administration. Using Bayesian posterior samples of key parameters extracted from the NLST-LDCT data, simulations on lead time were carried out on 16 hypothetical cohorts with four initial ages (55, 60, 65, and 70) and four future screening intervals (12, 18, 24, and 30 months). For each scenario, the estimated lead time for both screen-detected and interval cases is reported. Results show that the probability of no-early-detection (interval cases) increases monotonically when the screening interval increases for both genders. A male heavy smoker with an initial screening age at 60 has 11.65% (female 6.76%) chance of no-early-detection with annual screenings. This probability increases to 36.35% (female 28.26%) if the screenings were biennial. The mean lead time appears longer for women than for men. The mean lead time decreases when the screening interval increases, but it appears stable across different initial age groups. These results lay a foundation to evaluate survival benefit accurately with LDCT and to schedule future screening exams. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41666-018-0027-8) contains supplementary material, which is available to authorized users.

The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by Li et al. in 2013. In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design.

Objectives: The goal of this study is to investigate time durations in the disease-free state and the preclinical state of lung cancer for male and female smokers, using lung cancer data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Methods: We applied a modified likelihood function to the lung cancer data, to obtain maximum likelihood estimate and make Bayesian inference of the transition probability from the disease-free to the preclinical state, and the sojourn time distribution. The data was stratified by age and gender for smokers in the periodic screening program. A scaled Beta distribution was used for the transition probability density function, and a Weibull distribution was used to model the sojourn time in the preclinical state. Results: The epidemiological estimate of screening sensitivity is 0.649 for males and 0.68 for females. The transition probabilities are not the same for males and females: it is increasing monotonically to 80 years old for males; while it has a single maximum at age 72.5 for females. For male, the maximum likelihood estimate of mean sojourn time is 1.82 years, the Bayesian posterior mean and median sojourn time is 1.50 and 1.48 years, respectively. For female, the corresponding maximum likelihood estimate, posterior mean and median sojourn time are 1.84, 1.74 and 1.79 years respectively. The Bayesian mean lifetime risks for male and female smokers developing lung cancer are 12.0%, and 6.8%, respectively. Conclusion: Our estimation showed that male smokers are more susceptible to lung cancer, because they have a higher lifetime risk and higher transition probability density than the same aged female smokers. Once they enter into the preclinical state, the male smokers have a shorter mean sojourn time than the female, meaning that they are quicker to develop clinical symptom of lung cancer.

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study.

Objectives: Future outcomes of computed tomography in lung cancer screening were evaluated using recently derived probability formula in the disease progressive model, and the recently completed National Lung Screening Trial computed tomography (NLST-CT) data. Methods: Every participant in a screening program would fall into one of the four disjoint groups eventually: symptom-free-life, no-early-detection, true-early-detection and overdiagnosis, depending on whether he/she would be diagnosed with cancer and whether symptoms would have appeared before death. The probability of each outcome was a function of an individual’s current age, past and future screening frequency and the three key parameters: screening sensitivity, sojourn time and time in the disease-free state. The predictive probability was estimated for people with and without screening histories. Percentage of over-diagnosis among the screen-detected cases was also presented with human lifetime as a random variable. Results: The probability of heavy smokers to live a lung-cancer-free life would depend on their current age; it was about 80%, 86% and 94% for the 60, 70, and 80 years old respectively. The probabilities of no-early detection and true-early-detection were determined by the future screening interval and the current age: the probability of no-earlydetection would increase to about three times if the future screening interval changes from annual to biennial; while the probability of true-early-detection would decrease to about 75% if the future screening interval changes from annual to biennial. The probability of over-diagnosis among the screen-detected was increasing as people aging: ~3%, 5% and 9% for the 60, 70, and 80 years old correspondingly; this probability decreases slightly when the historic screening interval increases. Conclusion: This research provided the estimated probabilities of the four outcomes in the future and the percentage of overdiagnosis among the screen-detected cases. It provided a practical approach on the evaluation of long-term outcomes via CT in lung cancer screening.

Background Cancer screening has been effective in detecting tumors early before symptoms appear. However, the effectiveness of the regular fecal occult blood test (FOBT) in colorectal cancer in the long term has not been quantified. Methods We applied the statistical method developed by Wu and Rosner [1] using data from the Minnesota Colon Cancer Control Study (MCCCS). All initially asymptomatic participants were classified into four mutually exclusive groups: true-early-detection, no-early-detection, over-diagnosis, and symptom-free life; human lifetime was treated as a random variable and is subject to competing risks. All participants in the screening program will eventually fall into one of the four outcomes above. Predictive inferences on the percentages of the four outcomes for both genders were made using the Minnesota study data. Results Depending on gender, screening frequency and age at the initial screening, for all participants the probability of “symptom-free-life” varies between 95.3% and 96.6%; the probability of “true-early-detection” is 1.9–3.8%; the probability of no-early-detection is 0.3–2.0%; the probability of over-diagnosis is 0.16–0.3%. Among those with colorectal cancer detected by regular FOBT, the probability of over-diagnosis is lower than expected and is between 6% and 9%, with 95% CI (2.5%, 21.3%) for females and (1.9%, 44.7%) for males. The probability of true-early-detection increases as screening interval decreases. The probability of no-early-detection decreases as screening interval decreases. Conclusion The probability of over-diagnosis among the screen-detected cases is not as high as previously thought. We hope this outcome can provide valuable information on the effectiveness of the FOBT in colorectal cancer detection in the long term.

Breast cancer screening programs have been effective in detecting tumors prior to symptoms. Recently, there has been concern over the issue of over-diagnosis, that is, diagnosis of a breast cancer that does not manifest prior to death. Estimates for over-diagnosis vary, ranging from 7 to 52%. This variability may be due partially to issues associated with bias and/or incorrect inferences associated with the lack of probability modeling. A critical issue is how to evaluate the long-term effects due to continued screening. Participants in a periodic screening program can be classified into four mutually exclusive groups depending on whether individuals are diagnosed and whether their symptoms appear prior to death: True-earlydetection; No-early-detection; Over-diagnosis; and Not-sonecessary. All initially superficially healthy people will eventually fall into one of these four categories. This manuscript reviews the major methodologies associated with the over-diagnosis and long-term effects of breast cancer screening.

We estimated the effects on the lead time for women participating in a long-term breast cancer screening program when the screening sensitivities and disease progression are independent of age. The lead time, or time by which a diagnosis is advanced by screening, is one of the major concerns of any cancer screening program, which we consider to include both a mammogram and physical examination. Using estimates of test sensitivities and mean sojourn times previously calculated by other authors from observed data, we estimated properties of the lead time. We utilized the model for the lead time derived by other authors and ran simulations for different screening program designs, concentrating on screening interval lengths of 0.5 years, 1 year, 1.5 years, and 2 years. These estimates were based on a long-term screening program from age 50 to 80. For each six-month decrease in screening interval length, we estimated the percent increase in mean lead time, as well as the percent increase in the proportion of clinical patients who will have their cancer detected at a screening exam.

The association between screening sensitivity, transition probability, and individual's age in FOBT for colorectal cancer are explored, for both males and females.We apply the statistical method developed by Wu et al. [1] using the Minnesota colorectal cancer study group data, to make Bayesian inference for the age-dependent screening test sensitivity, the age-dependent transition probability from disease-free to preclinical state, and the sojourn time distribution, for both male and female participants in a periodic screening program. This gives us more information on the effectiveness of the fecal occult blood test in colorectal cancer detection.The sensitivity appears to increase with age for both genders. However, the posterior mean sensitivity is not monotonic with age for males; it has a peak around age 74. The standard errors of the sensitivity are not monotone either; there is a minimum at age 69 for males and at age 78 for females. The age-dependent transition probability is not a monotone function of age; it has a single maximum at age 72 for males and a single maximum at age 75 for females. The age dependency seems more dramatic for females than for males. The posterior mean sojourn time is 4.08 years for males and 2.41 years for females, with a posterior median of 1.66 years for males and 1.88 years for females. The 95% highest posterior density (HPD) interval is (0.97, 20.28) for males and (1.15, 5.96) for females, which are very large ranges, especially for males. The reason might be that there were fewer men than women in the annual screening program.Reliable estimates of age-dependent sensitivity and transition probability are of great value to policy-makers regarding the initial age for colorectal cancer screening exams. We found that the mean sojourn time for males is much longer than that for females, which may imply that FOBT screening for colorectal cancer may be more effective for males than for females.

The objective of this study was to investigate the moisture absorption process for wood-based composites subjected to over-saturated moisture conditions. Two stages are comprised in the moisture transfer process at the over-saturated moisture conditions, an initial stage which is the moisture transfer process mainly under fiber saturation point (FSP), and a second stage which is the moisture transfer process beyond the FSP. A model was developed based on two-part equations to describe the process, from which three coefficients (k1, k21, and k22) can be used to quantitatively describe the moisture transfer process under the conditions. Two different wood-based composites, wood fiberboard and wood fiber/polymer composites (polymer content: 30%), were used to test the model at four different ambient temperatures (30, 45, 62, and 80°C). It was shown that the two-part equation can accurately describe the moisture absorption process under over-saturated moisture conditions. The moisture absorption rate in the initial stage was about 30–60% greater than that in the second stage for most of the cases evaluated in this study. The higher the temperature, the greater moisture absorption parameters were obtained. At both moisture absorption stages (below FSP and above FSP), the calculated activation energy for the moisture absorption rate of wood fiberboard was very close to that of wood fiber/polymer composites.

This article develops a probability distribution for the lead time in periodic cancer screening examinations. The general aim is to allow statistical inference for a screening program's lead time, the length of time the diagnosis is advanced by screening. The program's lead time is distributed as a mixture of a point mass and a piecewise continuous distribution. Simulation studies using the HIP (Health Insurance Plan for Greater New York) study's data provide estimates of different characteristics of a screening program under different screening frequencies. The components of this mixture represent two aspects of screening's benefit, namely, a reduction in the number of interval cases and the extent by which screening advanced the age of diagnosis. We present estimates of these two measures for participants in a breast cancer screening program. We also provide the mean, mode, variance, and density curve of the program's lead time. The model can provide policy makers with important information regarding the screening period, frequency, and the endpoints that may serve as surrogates for the benefit to women who take part in a periodic screening program. Though the study focuses on breast cancer screening, it is also applicable to other kinds of chronic disease.

Generally, novices are taught fiberoptic intubation on patients by attending anesthesiologists; however, this approach raises patient safety concerns. Patient safety should improve if novice learners are trained for basic skills on simulators. In this educational study, we assessed the time and number of attempts required to train novices in fiberoptic bronchoscopy and fiberoptic intubation on simulators. Because decay in skills is inevitable, we also assessed fiberoptic bronchoscopy and fiberoptic intubation skill decay and the amount of effort required to regain fiberoptic bronchoscopy skill. First, we established attempt- and duration-based quantitative norms for reaching skill proficiency for fiberoptic bronchoscopy and fiberoptic intubation by experienced anesthesiologists (n = 8) and prepared an 11-step checklist and a 5-point global rating scale for assessment. Novice learners (n = 15) were trained to reach the established skill proficiency in a Virtual Reality simulator for fiberoptic bronchoscopy skills and a Human Airway Anatomy Simulator for fiberoptic intubation skills. Two months later, novices were reassessed to determine decay in learned skills and the required time to retrain them to fiberoptic bronchoscopy proficiency level. Proficiency in fiberoptic bronchoscopy skill level was achieved with 11 ± 5 attempts and after 658 ± 351 s. After 2 months without practice, the time taken by the novices to successful fiberoptic bronchoscopy on the Virtual Reality simulator increased from 41 ± 8 to 68 ± 31 s (P = 0.0138). Time and attempts required to retrain them were 424 ± 230 s and 9.1 ± 4.6 attempts, respectively. Novices were successfully trained to proficiency skill level. Although fiberoptic bronchoscopy skills started to decay within 2 months, the re-training time was shorter.

Purpose: The purpose of this paper is to examine the sensitivity and mean sojourn time specific lead time distribution in cancer screening trials when lifetime is a random variable in order to explore possible optimal initial age at screening and screening frequency. Methods: Summarized methods from Wu et al. (2012). Simulation was used in order to estimate the distribution of the lead time for a hypothetical individual with a future screening schedule. The lifetime distribution used comes from the Social Security Administration’s actuarial life tables. The lead time distribution was then calculated based on a loglogistic sojourn time distribution with two mean sojourn times (2, and 10 years), using three different initial screening ages, t0=40, 50, 60, different screening sensitivities (0.3 and 0.5 for men; 0.8 and 0.9 for women), and two different screening frequencies, one and two years for both men and women. Results: Smaller time intervals between screenings yield a smaller probability of no early detection and a greater expected lead time.

In addition to additive (A) and dominant (D) genetic effects, the A x A interaction (or A x A epistatic) effects that control many quantitative traits are important for genetic and breeding studies. To estimate these genetic variance components, including genotype x environment (G x E) interaction, one usually expects to have data from at least two generations (i.e., F 1 and F 2 ) and parents with the same entries in all environments. Practical difficulties may arise in implementing such a design. In this study, we performed Monte Carlo simulations to compare the estimated variance components between four partial and two complete genetic designs (GDs) using the mixed linear model approach. Our definition for GD is different from the traditional definitions of genetic mating designs. Simulation results showed that the estimated genetic variance components for A, A x E, A x A epistatic, and A X A x E effects were unbiased for the six designs. Among four partial designs, two provided the comparable results for D and D × E effects compared with the complete GDs, but with slightly larger mean square errors (MSEs), indicating that some partial GDs can be used when the genetic resources are limited.

A general simulation procedure is described to validate model fitting algorithms for complex likelihood functions that are utilized in periodic cancer screening trials. Although screening programs have existed for a few decades, there are still many unsolved problems, such as how age or hormone affects the screening sensitivity, the sojourn time in the preclinical state, and the transition probability from disease-free state to the preclinical state. Simulations are needed to check reliability or validity of the likelihood function combined with the associated effect functions. One bottleneck in the simulation procedure is the very time consuming calculations of the maximum likelihood estimates (MLE) from generated data. A practical procedure is presented, along with results for when both sensitivity and transition probability into the preclinical state are age-dependent. The procedure is also suitable for other applications.

Using a Bayesian approach to clinical trial design is becoming more common. For example, at the MD Anderson Cancer Center, Bayesian techniques are routinely employed in the design and analysis of Phase I and II trials. It is important that the operating characteristics of these procedures be determined as part of the process when establishing a stopping rule for a clinical trial. This study determines the power function for some common fixed-sample procedures in hypothesis testing, namely the one and two-sample tests involving the binomial and normal distributions. Also considered is a Bayesian test for multi-response (response and toxicity) in a Phase II trial, where the power function is determined.

The implementation of a Bayesian approach to wavelet regression that corresponds to the human visual system is examined. Most existing research in this area assumes non-informative priors, that is, a prior with mean zero. A new way is offered to implement prior information that mimics a visual inspection of noisy data, to obtain a first impression about the shape of the function that results in a prior with non-zero mean. This visually adaptive Bayesian (VAB) prior has a simple structure, intuitive interpretation, and is easy to implement. Skorohod topology is suggested as a more appropriate measure in signal recovering than the commonly used mean-squared error.

In this paper we present a new method called the NORM method for finding threshold values in wavelet regression. We use WaveThresh (G. Nason) software in S-Plus to implement this method, and compare it with existing methods, such as Donoho and Johnstone's SureShrink, AdaptShrink and Nason's cross-validation, and with optimal thresholding. The goal is to minimize the average mean-squared error. We use 3 different kinds of noise: iid normal, iid t variable, and correlated noise, on 8 different test applications. For iid normal noise, any method could be the best. The cross-validation method works best for independent long-tailed t noise. In the case of correlated noise, the NORM method behaves best when there is a large, positive lag one correlation. The SureShrink or AdaptShrink methods behave best when there is a large, negative lag one correlation. We give heuristic explanations of these behaviors. We also evaluate the accuracy of our method for estimation of noise level † .

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (

We applied a newly developed probability method to predict long term outcomes and over diagnosis in lung cancer screening using the Memorial Sloan-Kettering Cancer study (MSKC-LCSP) data. All participants were categorized into four mutually exclusive groups depending on their diagnosis status and ultimate disease status: symptom-freelife, no-early-detection, true-early-detection and over-diagnosis. Probability of each group is a function of the three key parameters: screening sensitivity, sojourn time in preclinical state and transition density from disease free to the preclinical state. We first obtained reliable and accurate estimates of these three key parameters using the MSKCLCSP data and likelihood function with a Bayesian approach, and then calculate the probability of each group by inserting these Bayesian posterior samples to the probability formulae, to predict future long term outcomes of lung cancer screening using chest x-ray. Human lifetime was treated as a random variable derived from US. Social Security Administration (SSA), so number of screening exams in the future is a random variable as well. The result shows that over diagnosis is not a big issue in lung cancer screening, given that it is only about 4.56% to 7.43% among the screendetected cases, depending on the age at the first screening.

The probability model for periodic screening was extended to provide statistical inference for sensitivity depending on sojourn time, in which the sensitivity was modeled as a function of time spent in the preclinical state and the sojourn time. The likelihood function with the proposed sensitivity model was then evaluated with simulated data to check its reliability in terms of the mean estimation and the standard error. Simulation results showed that the maximum likelihood estimates of the proposed model have little bias and small standard errors. The extended probability model was further applied to the Johns Hopkins Lung Project data using both maximum likelihood estimation and Bayesian Markov chain Monte Carlo.

BACKGROUND Childhood obesity has become a national public health crisis in America. Physical inactivity and unhealthy eating behaviors may contribute to the childhood obesity epidemic. School-based healthy lifestyle interventions play a promising role in preventing and controlling childhood obesity. A comprehensive school-based healthy lifestyle intervention was implemented in 4 rural elementary schools in Kentucky. METHODS The intervention included 4 goals: improving physical education, health education, family/community involvement, and school wellness policies. Children's physical activity was assessed by pedometer, and nutrition was assessed by a previous day recall survey in January (baseline), February (t1), March (t2), April (t3), and May (t4) of 2011. RESULTS The intervention had significant effects on increasing the percentages of children meeting physical activity (1% vs 5%, p

This paper extends the previous probability model for the distribution of lead time in periodic cancer screening exams, namely, in that the lifetime T is treated as a random variable, instead of a fixed value. Hence the number of screens for a given individual is a random variable as well. We use the actuarial life table from the Social Security Administration to obtain the lifetime distribution, and then use this information to project the lead time distribution for someone with a future screening schedule. Simulation studies using the HIP study group data provide estimates of the lead time under different screening frequencies. The projected lead time has two components: a point mass at zero (corresponding to interval cases detected between screening exams) and a continuous probability density. We present estimates of the projected lead time for participants in a breast cancer screening program. The model is more realistic and can inform optimal screening frequency. This study focuses on breast cancer screening, but is applicable to other kinds of cancer screening also.

We investigate the efficacy of the dual-screening, annual chest X-ray and 4-monthly sputum cytology, for lung cancer detection using the Johns Hopkins Lung Project (JHLP) data. An advanced statistical approach to testing the dependency of dual diagnostic tests, chest X-ray and sputum cytology is applied for lung cancer detection. To achieve this, the screening sensitivities of X-ray only, cytology only, and dual-screening, the overall transition probability, the sojourn time, and the mean lead time were estimated from JHLP data. A derivative-free global optimization method, Particle Swarm Optimization (PSO), was further employed in the procedure to avoid being stuck at a local optimum or the boundary. Our analysis shows that the overall sensitivity of lung cancer screening is 85.34% with chest X-ray of 79.93% and 4-monthly sputum cytology of 26.98%, resulting in a small correlation coefficient of -0.0039, which is not significantly different from zero. As a result, the dual procedure improves the overall screening sensitivity up to ~5% in lung cancer detection.