Changes in Preservice Teachers’ Self-Efficacy: From Science Methods to Student Teaching

The purpose of this research was to assess preservice teachers self-efficacy at different stages of their educational career in an attempt to determine the extent to which self-efficacy beliefs may change over time. In addition, the critical incidents, which may contribute to changes in self-efficacy, were also investigated. The instrument used in the study was the Teaching Science as Inquiry (TSI) Instrument. The TSI Instrument was administered to 38 preservice elementary teachers to measure the self-efficacy beliefs of the teacher participants in regard to the teaching of science as inquiry. Based on the results and the associated data analysis, mean and median values demonstrate positive change for self-efficacy and outcome expectancy throughout the data collection period.

Auditory verbal hallucinations: imaging, analysis, and intervention

In this article, we will link neuroimaging, data analysis, and intervention methods in an important psychiatric condition: auditory verbal hallucinations (AVH). The clinical and phenomenological background as well as neurophysiological findings will be covered and discussed with respect to noninvasive brain stimulation. Additionally, methods of noninvasive brain stimulation will be presented as ways to intervene with AVH. Finally, preliminary conclusions and possible future perspectives will be proposed.

Data Quality Assessment of Ungated Flow Cytometry Data in High

Background: The recent development of semi-automated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data. Methods: We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized functions and methods in the Bioconductor package rflowcyt. We demonstrate the use of these approaches by investigating two independent sets of high throughput flow cytometry data. Results: We found that graphical representations can reveal substantial non-biological differences in samples. Empirical Cumulative Distribution Function and summary scatterplots were especially useful in the rapid identification of problems not identified by manual review. Conclusions: Graphical exploratory data analytic tools are quick and useful means of assessing data quality. We propose that the described visualizations should be used as quality assessment tools and where possible, be used for quality control.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

CA15-3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven International Breast Cancer Study Group trials

BACKGROUND: We evaluated the ability of CA15-3 and alkaline phosphatase (ALP) to predict breast cancer recurrence. PATIENTS AND METHODS: Data from seven International Breast Cancer Study Group trials were combined. The primary end point was relapse-free survival (RFS) (time from randomization to first breast cancer recurrence), and analyses included 3953 patients with one or more CA15-3 and ALP measurement during their RFS period. CA15-3 was considered abnormal if >30 U/ml or >50% higher than the first value recorded; ALP was recorded as normal, abnormal, or equivocal. Cox proportional hazards models with a time-varying indicator for abnormal CA15-3 and/or ALP were utilized. RESULTS: Overall, 784 patients (20%) had a recurrence, before which 274 (35%) had one or more abnormal CA15-3 and 35 (4%) had one or more abnormal ALP. Risk of recurrence increased by 30% for patients with abnormal CA15-3 [hazard ratio (HR) = 1.30; P = 0.0005], and by 4% for those with abnormal ALP (HR = 1.04; P = 0.82). Recurrence risk was greatest for patients with either (HR = 2.40; P < 0.0001) and with both (HR = 4.69; P < 0.0001) biomarkers abnormal. ALP better predicted liver recurrence. CONCLUSIONS: CA15-3 was better able to predict breast cancer recurrence than ALP, but use of both biomarkers together provided a better early indicator of recurrence. Whether routine use of these biomarkers improves overall survival remains an open question.

From text to codings: intercoder reliability assessment in qualitative content analysis

BACKGROUND: High intercoder reliability (ICR) is required in qualitative content analysis for assuring quality when more than one coder is involved in data analysis. The literature is short of standardized procedures for ICR procedures in qualitative content analysis. OBJECTIVE: To illustrate how ICR assessment can be used to improve codings in qualitative content analysis. METHODS: Key steps of the procedure are presented, drawing on data from a qualitative study on patients' perspectives on low back pain. RESULTS: First, a coding scheme was developed using a comprehensive inductive and deductive approach. Second, 10 transcripts were coded independently by two researchers, and ICR was calculated. A resulting kappa value of .67 can be regarded as satisfactory to solid. Moreover, varying agreement rates helped to identify problems in the coding scheme. Low agreement rates, for instance, indicated that respective codes were defined too broadly and would need clarification. In a third step, the results of the analysis were used to improve the coding scheme, leading to consistent and high-quality results. DISCUSSION: The quantitative approach of ICR assessment is a viable instrument for quality assurance in qualitative content analysis. Kappa values and close inspection of agreement rates help to estimate and increase quality of codings. This approach facilitates good practice in coding and enhances credibility of analysis, especially when large samples are interviewed, different coders are involved, and quantitative results are presented.

Multidimensional preventive home visit programs for community-dwelling older adults: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND: Multidimensional preventive home visit programs aim at maintaining health and autonomy of older adults and preventing disability and subsequent nursing home admission, but results of randomized controlled trials (RCTs) have been inconsistent. Our objective was to systematically review RCTs examining the effect of home visit programs on mortality, nursing home admissions, and functional status decline. METHODS: Data sources were MEDLINE, EMBASE, Cochrane CENTRAL database, and references. Studies were reviewed to identify RCTs that compared outcome data of older participants in preventive home visit programs with control group outcome data. Publications reporting 21 trials were included. Data on study population, intervention characteristics, outcomes, and trial quality were double-extracted. We conducted random effects meta-analyses. RESULTS: Pooled effects estimates revealed statistically nonsignificant favorable, and heterogeneous effects on mortality (odds ratio [OR] 0.92, 95% confidence interval [CI], 0.80-1.05), functional status decline (OR 0.89, 95% CI, 0.77-1.03), and nursing home admission (OR 0.86, 95% CI, 0.68-1.10). A beneficial effect on mortality was seen in younger study populations (OR 0.74, 95% CI, 0.58-0.94) but not in older populations (OR 1.14, 95% CI, 0.90-1.43). Functional decline was reduced in programs including a clinical examination in the initial assessment (OR 0.64, 95% CI, 0.48-0.87) but not in other trials (OR 1.00, 95% CI, 0.88-1.14). There was no single factor explaining the heterogenous effects of trials on nursing home admissions. CONCLUSION: Multidimensional preventive home visits have the potential to reduce disability burden among older adults when based on multidimensional assessment with clinical examination. Effects on nursing home admissions are heterogeneous and likely depend on multiple factors including population factors, program characteristics, and health care setting.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Common Mistakes When Fitting Models to Data

Dr. Rossi discusses the common errors that are made when fitting statistical models to data. Focuses on the planning, data analysis, and interpretation phases of a statistical analysis, and highlights the errors that are commonly made by researchers of these phases. The implications of these commonly made errors are discussed along with a discussion of the methods that can be used to prevent these errors from occurring. A prescription for carrying out a correct statistical analysis will be discussed.

Recombinant human erythropoiesis-stimulating agents and mortality in patients with cancer: a meta-analysis of randomised trials

BACKGROUND: Erythropoiesis-stimulating agents reduce anaemia in patients with cancer and could improve their quality of life, but these drugs might increase mortality. We therefore did a meta-analysis of randomised controlled trials in which these drugs plus red blood cell transfusions were compared with transfusion alone for prophylaxis or treatment of anaemia in patients with cancer. METHODS: Data for patients treated with epoetin alfa, epoetin beta, or darbepoetin alfa were obtained and analysed by independent statisticians using fixed-effects and random-effects meta-analysis. Analyses were by intention to treat. Primary endpoints were mortality during the active study period and overall survival during the longest available follow-up, irrespective of anticancer treatment, and in patients given chemotherapy. Tests for interactions were used to identify differences in effects of erythropoiesis-stimulating agents on mortality across prespecified subgroups. FINDINGS: Data from a total of 13 933 patients with cancer in 53 trials were analysed. 1530 patients died during the active study period and 4993 overall. Erythropoiesis-stimulating agents increased mortality during the active study period (combined hazard ratio [cHR] 1.17, 95% CI 1.06-1.30) and worsened overall survival (1.06, 1.00-1.12), with little heterogeneity between trials (I(2) 0%, p=0.87 for mortality during the active study period, and I(2) 7.1%, p=0.33 for overall survival). 10 441 patients on chemotherapy were enrolled in 38 trials. The cHR for mortality during the active study period was 1.10 (0.98-1.24), and 1.04 (0.97-1.11) for overall survival. There was little evidence for a difference between trials of patients given different anticancer treatments (p for interaction=0.42). INTERPRETATION: Treatment with erythropoiesis-stimulating agents in patients with cancer increased mortality during active study periods and worsened overall survival. The increased risk of death associated with treatment with these drugs should be balanced against their benefits. FUNDING: German Federal Ministry of Education and Research, Medical Faculty of University of Cologne, and Oncosuisse (Switzerland).

Three-year cost analysis of function-centred versus pain-centred inpatient rehabilitation in patients with chronic non-specific low back pain

OBJECTIVE: To compare costs of function- and pain-centred inpatient treatment in patients with chronic low back pain over 3 years of follow-up. DESIGN: Cost analysis of a randomized controlled trial. PATIENTS: A total of 174 patients with chronic low back pain were randomized to function- or pain-centred inpatient treatment. METHODS: Data on direct and indirect costs were gathered by questionnaires sent to patients, health insurance providers, employers, and the Swiss Disability Insurance Company. RESULTS: There was a non-significant difference in total medical costs after 3 years' follow-up. Total costs were 77,305 Euros in the function-centred inpatient treatment group and 83,085 Euros in the pain-centred inpatient treatment group. Likewise, indirect costs after 3 years from lost work days were non-significantly lower in the function-centred in-patient treatment group (6354 Euros; 95% confidence interval -20,892, 8392) and direct medical costs were non-significantly higher in the function-centred inpatient treatment group (574 Euros; 95% confidence interval -862, 2011). CONCLUSION: The total costs of function-centred and pain-centred inpatient treatment were similar over the whole 3-year follow-up.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.