Modified check loss for efficient estimation via model selection in quantile regression.

The check loss function is used to define quantile regression. In cross-validation, it is also employed as a validation function when the true distribution is unknown. However, our empirical study indicates that validation with the check loss often leads to overfitting the data. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. This has the effect of guarding against overfitting to some extent. The adjustment is devised to shrink to zero as sample size grows. Through various simulation settings of linear and nonlinear regressions, the improvement due to modification of the check loss by quadratic adjustment is examined empirically.

Transformed low-rank ANOVA models for high-dimensional variable selection.

High-dimensional data are often encountered in biomedical, environmental, and other studies. For example, in biomedical studies that involve high-throughput omic data, an important problem is to search for genetic variables that are predictive of a particular phenotype. A conventional solution is to characterize such relationships through regression models in which a phenotype is treated as the response variable and the variables are treated as covariates; this approach becomes particularly challenging when the number of variables exceeds the number of samples. We propose a general framework for expressing the transformed mean of high-dimensional variables in an exponential distribution family via ANOVA models in which a low-rank interaction space captures the association between the phenotype and the variables. This alternative method transforms the variable selection problem into a well-posed problem with the number of observations larger than the number of variables. In addition, we propose a model selection criterion for the new model framework with a diverging number of parameters, and establish the consistency of the selection criterion. We demonstrate the appealing performance of the proposed method in terms of prediction and detection accuracy through simulations and real data analyses.

Effect of statin and aspirin use on toxicity and pathological complete response rate of neo-adjuvant chemoradiation for rectal cancer.

AIM: To retrospectively evaluate the potential impact of statin and aspirin use on acute toxicity and pathological complete response (pCR) rate in rectal cancer patients receiving neo-adjuvant long-course radiation therapy (LCRT) with concurrent chemotherapy. METHODS: A retrospective review was performed of all patients undergoing neo-adjuvant LCRT for rectal adenocarcinoma at the Regional Cancer Treatment Service between 1 September 2007 and 1 June 2011. Data obtained include demographic details; date and radiological TNM stage at diagnosis; medication taken at time of RT; toxicity during LCRT; and surgical histology to determine if a pCR was obtained following LCRT. RESULTS: Neo-adjuvant LCRT was administered to 142 patients for rectal cancer during this period; concurrent chemotherapy was omitted in 13 due to significant comorbidities. TNM stage was 2 or 3 radiologically at diagnosis in 127 (89.4%) of patients. At the time of LCRT, 23% were taking a statin and 25% were taking aspirin. Of 135 assessable patients, 34 (13%) achieved a pCR at surgery. On logistic regression, pCR was not significantly associated with the use of chemotherapy, statins, aspirin, other NSAIDs, T-stage or N-stage. There was no significant correlation between statin or aspirin use with bladder or rectal toxicity. Actuarial time to maximum rectal toxicity was not different in statin users or nonusers. CONCLUSION: In contrast to other larger retrospective series, this study did not find improvements in toxicity or pCR rate through statin or aspirin use in rectal cancer patients undergoing LCRT. Their potential benefits in this setting would be best studied prospectively in a large randomized trial.

A K-fold Averaging Cross-validation Procedure.

Cross-validation type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold cross validation procedure to select a candidate 'optimal' model from each hold-out fold and average the K candidate 'optimal' models to obtain the ultimate model. Due to the averaging effect, the variance of the proposed estimates can be significantly reduced. This new procedure results in more stable and efficient parameter estimation than the classical K-fold cross validation procedure. In addition, we show the asymptotic equivalence between the proposed and classical cross validation procedures in the linear regression setting. We also demonstrate the broad applicability of the proposed procedure via two examples of parameter sparsity regularization and quantile smoothing splines modeling. We illustrate the promise of the proposed method through simulations and a real data example.

Impact of concomitant surgical atrial fibrillation ablation in patients undergoing aortic valve replacement.

BACKGROUND: The clinical benefit of concomitant atrial fibrillation (AF) ablation at the time of aortic valve replacement (AVR) is uncertain. METHODS AND RESULTS: A total of 124 patients with AF who underwent AVR with (n=50) or without (n=74) a concomitant maze procedure, between 2000 and 2011, were evaluated. There were no significant differences in early postoperative outcomes. During a median clinical follow-up of 18.1 months (interquartile range: 6.9-47.8 months), 19 late deaths (15.3%) and 33 valve-related complications (26.6%) occurred, but the differences between groups were not statistically significant. Major event-free survival at 5 years was 60.9±9.9% vs. 57.0±10.3% (P=0.41). After adjustment, the maze group demonstrated similar risks for major adverse cardiac events (hazard ratio, 1.18; 95% confidence interval, 0.56-2.49; P=0.67). However, the rate of sinus rhythm restoration at 4 years was significantly higher in the maze group (80.6% vs. 3.6%, P<0.001). Left atrial dimension was smaller (46.9 vs. 50.4mm, P=0.017), and the ejection fraction was higher (60.6% vs. 58.0%, P=0.059) in the maze group. The rate of postoperative anticoagulation was also lower in the maze group (53.1% vs. 89.2%, P<0.001). CONCLUSIONS: Concomitant AF ablation in patients undergoing AVR resulted in increased sinus rhythm restoration, better echocardiographic results, and decreased anticoagulation requirement, without increasing surgical morbidity or mortality.

Oncology nurses' knowledge of survivorship care planning: the need for education.

PURPOSE/OBJECTIVES: To survey nurses about their knowledge of cancer survivorship care. DESIGN: Descriptive, cross-sectional. SETTING: Midwestern comprehensive cancer center. SAMPLE: 223 registered and advanced practice nurses. METHODS: Online survey of survivorship knowledge using a 50-item questionnaire derived from the Institute of Medicine report and related publications. MAIN RESEARCH VARIABLES: Concepts of survivorship care and common long-term symptoms. FINDINGS: Most nurses reported having knowledge about healthy lifestyle habits; more than 50% of nurses reported having knowledge about chemotherapy, surgery, and radiation therapy, as well as side effects of fatigue, depression, limitations of daily activities, and weight gain; less than 50% of nurses reported having knowledge of impact on family, biologic agents, lymphedema, immunizations or vaccinations, and osteoporosis screening; less than 40% of nurses reported having knowledge about marital and partner relationships, osteoporosis prevention and care, sexuality, side effects of bone marrow transplantation, employment issues, and angiogenesis agents; and less than 25% of nurses reported having knowledge on genetic risks, as well as fertility, financial, and insurance issues. CONCLUSIONS: Oncology nurses at an academic comprehensive cancer center reported gaps in knowledge consistent with previous studies about knowledge of survivorship care. IMPLICATIONS FOR NURSING: The Institute of Medicine has challenged oncology providers to address cancer survivorship care planning. Gaps in cancer survivorship knowledge are evident and will require focused education for this initiative to be successful.

Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA.

In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this paper, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection.

In-frame cDNA library combined with protein complementation assay identifies ARL11-binding partners.

The cDNA expression libraries that produce correct proteins are essential in facilitating the identification of protein-protein interactions. The 5'-untranslated regions (UTRs) that are present in the majority of mammalian and non-mammalian genes are predicted to alter the expression of correct proteins from cDNA libraries. We developed a novel cDNA expression library from which 5'-UTRs were removed using a mixture of polymerase chain reaction primers that complement the Kozak sequences we refer to as an "in-frame cDNA library." We used this library with the protein complementation assay to identify two novel binding partners for ras-related ADP-ribosylation factor-like 11 (ARL11), cellular retinoic acid binding protein 2 (CRABP2), and phosphoglycerate mutase 1 (PGAM1). Thus, the in-frame cDNA library without 5'-UTRs we describe here increases the chance of correctly identifying protein interactions and will have wide applications in both mammalian and non-mammalian detection systems.