ROC Surfaces in the Presence of Verification Bias

In diagnostic medicine, the Receiver Operating Characteristic (ROC) surface is one of the established tools for assessing the accuracy of a diagnostic test in discriminating three disease states, and the volume under the ROC surface has served as a summary index for diagnostic accuracy. In practice, the selection for definitive disease examination may be based on initial test measurements, and induces verification bias in the assessment. We propose here a nonparametric likelihood-based approach to construct the empirical ROC surface in the presence of differential verification, and to estimate the volume under the ROC surface. Estimators of the standard deviation are derived by both the Fisher\u27s Information and Jack-knife method, and their relative accuracy is evaluated in an extensive simulation study. The methodology is further extended to incorporate discrete baseline covariates in the selection process, and to compare the accuracy of a pair of diagnostic tests. We apply the proposed method to compare the diagnostic accuracy between Mini-Mental State Examination and clinical evaluation of dementia, in discriminating among three disease states of Alzheimer\u27s disease

Bayesian approaches to joint longitudinal and survival models accommodating both zero and nonzero cure fractions

The trade-offs between survival benefits and therapeutic adverse effects on quality of life (QOL) is always an important clinical issue for cancer and AIDS patients. The International Breast Cancer Study Group (IBCSG) conducted a large clinical trial, IBCSG Trial VI, to examine the duration and timing of adjuvant ther-apy for advanced breast cancer patients after the initial removal surgery. We present a novel joint model for longitudinal and survival data to evaluate the relationship between QOL and breast cancer progression, and also assess issues associated with different therapeutic procedures and baseline covariates. Multidimensional longi-tudinal QOL measurements are modeled in a hierarchical mixed effects model to account for psychological fluctuations and measurement errors, provide estimates for time points where QOL data are not available, and to explicitly allow for direct inferences about different dependence structures in the QOL data over time and over different QOL measures (indicators). A parametric survival model is also pro-posed for disease-free survival (DFS) to incorporate the underlying smooth QOL trajectories and prognostic factors. This survival model is attractive and capable of accommodating both zero and nonzero cure fractions. With advances in modern medicine, a positive cure fraction is often tenable for breast cancer patients since many are completely cured after surgery, and are no longer susceptible to relapse. A Bayesian paradigm is adopted to facilitate the estimation process and ease the computational complexity

A new class of mixture models for differential gene expression in DNA microarray data

One of the fundamental issues in analyzing microarray data is to determine which genes are expressed and which ones are not for a given group of subjects. In datasets where many genes are expressed and many are not expressed (i.e., underexpressed), a bimodal distribution for the gene expression levels often results, where one mode of the distribution represents the expressed genes and the other mode represents the underexpressed genes. To model this bimodality, we propose a new class of mixture models that utilize a random threshold value for accommodating bimodality in the gene expression distribution. Theoretical properties of the proposed model are carefully examined. We use this new model to examine the problem of differential gene expression between two groups of subjects, develop prior distributions, and derive a new criterion for determining which genes are differentially expressed between the two groups. Prior elicitation is carried out using empirical Bayes methodology in order to estimate the threshold value as well as elicit the hyperparameters for the two component mixture model. The new gene selection criterion is demonstrated via several simulations to have excellent false positive rate and false negative rate properties. A gastric cancer dataset is used to motivate and illustrate the proposed methodology

Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data

This article develops a variety of influence measures for carrying out perturbation (or sensitivity) analysis to joint models of longitudinal and survival data (JMLS) in Bayesian analysis. A perturbation model is introduced to characterize individual and global perturbations to the three components of a Bayesian model, including the data points, the prior distribution, and the sampling distribution. Local influence measures are proposed to quantify the degree of these perturbations to the JMLS. The proposed methods allow the detection of outliers or influential observations and the assessment of the sensitivity of inferences to various unverifiable assumptions on the Bayesian analysis of JMLS. Simulation studies and a real data set are used to highlight the broad spectrum of applications for our Bayesian influence methods

Confidence regions for repeated measures ANOVA power curves based on estimated covariance

Abstract Background Using covariance or mean estimates from previous data introduces randomness into each power value in a power curve. Creating confidence intervals about the power estimates improves study planning by allowing scientists to account for the uncertainty in the power estimates. Driving examples arise in many imaging applications. Methods We use both analytical and Monte Carlo simulation methods. Our analytical derivations apply to power for tests with the univariate approach to repeated measures (UNIREP). Approximate confidence intervals and regions for power based on an estimated covariance matrix and fixed means are described. Extensive simulations are used to examine the properties of the approximations. Results Closed-form expressions are given for approximate power and confidence intervals and regions. Monte Carlo simulations support the accuracy of the approximations for practical ranges of sample size, rank of the design matrix, error degrees of freedom, and the amount of deviation from sphericity. The new methods provide accurate coverage probabilities for all four UNIREP tests, even for small sample sizes. Accuracy is higher for higher power values than for lower power values, making the methods especially useful in practical research conditions. The new techniques allow the plotting of power confidence regions around an estimated power curve, an approach that has been well received by researchers. Free software makes the new methods readily available. Conclusions The new techniques allow a convenient way to account for the uncertainty of using an estimated covariance matrix in choosing a sample size for a repeated measures ANOVA design. Medical imaging and many other types of healthcare research often use repeated measures ANOVA

Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes: Overall power for HDLSS

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high-dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high-dimensional pathway analysis. With an account of any complex correlation structure between high-dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite-sample) and approximate non-null distributions of the ‘univariate’ approach to repeated measures test statistic, as well as power-equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects

State Variation in Squamous Cell Carcinoma of the anus incidence and Mortality, and association With Hiv/Aids and Smoking in the United States

PURPOSE: Squamous cell carcinoma of the anus (SCCA) incidence and mortality rates are rising in the United States. Understanding state-level incidence and mortality patterns and associations with smoking and AIDS prevalence (key risk factors) could help unravel disparities and provide etiologic clues. METHODS: Using the US Cancer Statistics and the National Center for Health Statistics data sets, we estimated state-level SCCA incidence and mortality rates. Rate ratios (RRs) were calculated to compare incidence and mortality in 2014-2018 versus 2001-2005. The correlations between SCCA incidence with current smoking (from the Behavioral Risk Factor Surveillance System) and AIDS (from the HIV Surveillance system) prevalence were evaluated using Spearman\u27s rank correlation coefficient. RESULTS: Nationally, SCCA incidence and mortality rates (per 100,000) increased among men (incidence, 2.29-3.36, mortality, 0.46-0.74) and women (incidence, 3.88-6.30, mortality, 0.65-1.02) age ≥ 50 years, but decreased among men age \u3c 50 years and were stable among similar-aged women. In state-level analysis, a marked increase in incidence (≥ 1.5-fold for men and ≥ two-fold for women) and mortality (≥ two-fold) for persons age ≥ 50 years was largely concentrated in the Midwestern and Southeastern states. State-level SCCA incidence rates in recent years (2014-2018) among men were correlated ( CONCLUSION: During 2001-2005 to 2014-2018, SCCA incidence and mortality nearly doubled among men and women age ≥ 50 years living in Midwest and Southeast. State variation in AIDS and smoking patterns may explain variation in SCCA incidence. Improved and targeted prevention is needed to combat the rise in SCCA incidence and mitigate magnifying geographic disparities

Results of Treatment for Patients With Multicentric or Bilaterally Predisposed Unilateral Wilms Tumor (AREN0534): A report from the Children’s Oncology Group

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156248/2/cncr32958_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156248/1/cncr32958.pd

Alveolar Rhabdomyosarcoma with Regional Nodal Involvement: Results of a Combined Analysis from Two Cooperative Groups

BACKGROUND: Treatment of children and adolescents with alveolar rhabdomyosarcoma (ARMS) and regional nodal involvement (N1) have been approached differently by North American and European cooperative groups. In order to define the better therapeutic strategy, we analyzed two studies conducted between 2005 and 2016 by the European paediatric Soft tissue sarcoma Study Group (EpSSG) and Children’s Oncology Group (COG). METHODS: We retrospectively identified patients with ARMS-N1 enrolled in either EpSSG RMS2005 or in COG ARST0531. Chemotherapy in RMS2005 comprised IVADo (ifosfamide, vincristine, dactinomycin, doxorubicin), IVA and maintenance (vinorelbine, cyclophosphamide); in ARST0531 it consisted on either VAC (vincristine, dactinomycin, cyclophosphamide) or VAC alternating with VI (vincristine, irinotecan). Local treatment was similar in both protocols. RESULTS: The analysis of the clinical characteristics of 239 patients showed some differences between study groups: in RMS2005, advanced IRS Group and large tumors predominated. There were no differences in outcomes between the two groups: 5-year event-free survival (EFS), 49%(95%CI=39–59) and 44%(95%CI=30–58), and overall survival (OS), 51%(95%CI=41–61) and 53.6%(95%CI=40–68), in RMS2005 and ARST0531, respectively. In RMS2005, EFS of patients with FOXO1-positive tumors was significantly inferior to those FOXO1-negative (49.3% vs 73%, p=0.034). In contrast, in ARST0531, EFS of patients with FOXO1-positive tumors was 45% compared with 43.8% for those FOXO1-negative. CONCLUSIONS: The outcome of patients with ARMS N1 was similar in both protocols. However, patients with FOXO1 fusion-negative tumors enrolled in RMS2005 showed a significantly better outcome, suggesting that different strategies of chemotherapy may have an impact in the outcome of this subgroup of patients