One-way ANOVA Model Research Articles

One-way ANOVA Model with Underreported Counts

The focus of this article is on testing the equality of treatment means in a one-way analysis of variance setting with count response. The count data is further subject to misclassification, resulting in undercount of the event. Four asymptotically optimal tests are developed after correcting for the underreporting of the count. A simulation study is conducted to compare how these tests compete with each other with respect to level and power. The impact of ignoring undercount on the level of the tests is also investigated through this study. A comparison of these tests with the normal theory-based inference is also done after appropriate transformation of count data to a continuous measurement.

Self Service Technology in Online Financial Services

The objectives of the research paper are determining the factors influencing the customers to use online financial services, the reasons for customers for choosing and not choosing online financial services, the effect of customer satisfaction and to understand the factors to be added to the existing online financial services which in turn will increase a positive customer base. The problems that has been found is the financial websites are difficult & inconvenient to use due to less online features, usage outdated software, slow transfer of money, security issues of customer details, lack of personal touch of the customers to the financial institutions. There has always been accessibility issue and technical issues and unavailability of notarization and customized website links for online financial services. The statistical tools used for analysis these issues are frequency analysis tool, chi-square analysis, one-way ANOVA and SEM model to find the relationship between variables. The findings were depicted that the future of online financial services will be at a rapid growth and thus we will have to rectify the current issues in it. The suggestions in this research state that if the above issues are rectified along with making the untapped population literate, online financial services will be a biggest market in India.

Predicting Non-Responders to Immunotherapy Treatments through Simulation of NGS Information

Introduction: Immunotherapy is an exciting new option to stimulate a "host-vs.-cancer" effect. Such treatment regimens are dominated by Programmed Death-1 (PD-1) and Programmed Death Ligand-1 (PD-L1) immune checkpoint inhibitors. However, not all patients respond to PD-1/PD-L1 inhibition as confirmed by results from an "all-comers" clinical strategy. These inherent clinical gaps are addressed in studies by incorporating PD-L1 expression as inclusion criteria and combining PD-1/PD-L1 inhibitors with other drug agents. Unfortunately these outcomes have not been clinically beneficial and cause unnecessary toxicity to patients and unmet needs.Here we develop personalized methodologies to predict a response to PD-L1/PD-1 inhibitors based on two factors. First, PD-L1 is not the only immune checkpoint; other signaling between cancer and immune cells needs to be incorporated to predict non-responders to a PD-L1/PD-1 inhibitor. Second, precision medicine and personalization is driven by patient tumor genomics. A recent retrospective study correlated NRAS with PD-L1/PD-1 treatment responses. Beyond a single gene mutation or aberration, the holistic consideration of all gene mutations, copy number variations, and methylation status would impact immune signal activators and inhibitors from the cancer cell.We used a predictive simulation model of multiple myeloma and dendritic cells with corresponding in-vitro models. We developed an immunotherapy response phenotype, which is a function of bio-markers representing immune evasion, immune activation, metastasis, and dendritic cell infiltration in cancer cells. We then modeled two myeloma cell lines using available genomics information as proxy for myeloma patient cancer. Finally, we predicted these two representative patient classes would vary in response to PD-L1/PD-1 inhibitors. We validated our prediction that bio-markers contribute to the immunotherapy response.Methods: Predictive computational simulation models of myeloma cell lines, human myeloid dendritic cells, and myeloma cell + dendritic cell co-cultures were developed and used to predict extracellular (IL6, IL10, TGFB1, VEGFA) and cell-associated (CD47, FASLG, IDO1, PD-L1) biomarker readouts in an automated high-throughput system. Results were validated with myeloma cell lines MM.IS and U266B1 cultivated with and without dendritic cells in Transwell 12-well polystyrene plates. At 24 hours, IL6, IL10, TGFB1, and VEGFA concentrations in tissue culture media were determined using Millipore immunoassays. CD47, FASLG, IDO1, and PD-L1 concentrations in cell lysates were determined by ELISA and IHC. One-way fixed-effects ANOVA models were fit to log-transformed concentrations. Pairwise group comparisons were conducted using Tukey's Honest Significant Differences (JMP10, Version 10.0, SAS, Cary, NC) at a 0.05 level of significance.Results: Predictive simulation and experimental results were highly correlated. Predicted IL10, TGFβ1, IDO1, CD47, FASLG, IL6, VEGFA, and PD-L1 responses differed among MM.IS and U266B1 patient models and were similar to those observed in cell culture supernatants and cell lysates.Simulation models also predicted myeloma cell effects on dendritic cell biomarker responses. Twenty-three myeloma cell lines had high, moderate, and low expression effects on 19 dendritic cell biomarker readout responses. Experimental results using MM.IS and U266B1 cell lines + dendritic cell co-cultures confirmed these effects also occur in cell co-culture. Predicted IL10, TGFβ1, IDO1, CD47, FASLG, IL6, VEGFA, and PD-L1 responses were similar to that observed in cell culture supernatants and cell lysates.Conclusion: The ability to predict which patients respond to PD-L1/PD-1 inhibitors and adjuvant combinations of existing drugs will improve response rates and meet current needs. Here we show a novel predictive simulation based approach with a myeloma example to leverage patient genomics information for predicting non-responders. This approach predicts the immunotherapy index of a patient, which is correlated experimentally. We show that U266B1 has higher PD-L1 expression because of patient genomics. While correlation at the bio-marker level is important, future work will focus on predicting and confirming the response to PD-L1 inhibitors. DisclosuresBrogden:Cellworks Group Inc.: Research Funding. Fischer:Cellworks Group Inc.: Research Funding. Bates:Cellworks Group Inc.: Research Funding. Lanzel:Cellworks Group Inc.: Research Funding. Gomez Hernandez:Cellworks Group Inc.: Research Funding. Treinen:Cellworks Group Inc.: Research Funding. Recker:Cellworks Group Inc.: Research Funding. Nair:Cellworks Group Inc.: Employment. Sundara:Cellworks Group Inc.: Employment. Joseph:Cellworks Group Inc.: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership. Vali:Cellworks Group, Inc.: Employment, Equity Ownership.

Adoption E-Commerce for Export Market of Small and Medium Enterprises in Thailand

This research aims to analyze small and medium enterprises (SMEs) who are exporters in Thailand to e-commerce adoption for export market. The study presents four hypotheses in order to understand the point of view of SMEs to e-commerce adoption into global marketplace. The author collected the data by questionnaires sent to prospect SMEs. The usable questionnaires with complete information reached at 21.5% of total questionnaires. For statistics analysis, the study applies t-test, One-way ANOVA, Chi-square test and simple regression model to examine the hypotheses. The results showed SME's realization between exporter and non-exporter and also different size of SME exporters toward the benefit of e-commerce to export market being as the same at the high level. The study also presented the relationship between SME exporter and e-commerce adoption which is dependent significantly. In addition, this research concluded that e-commerce adoption of Thai SME exporters does not impact positively to export intensity significantly. The findings of the study suggested Thai SMEs both exporter and non-exporter now realize the great benefits of e-commerce to global market at high level and nowadays employ e-commerce as one of a strategic management to their global commercial. However, e-commerce adoption of SMEs has not increased the export intensity nevertheless. It probably implies SME exporters have not strongly took advantage of e-commerce to export market substantially. With data analysis, e-commerce of Thai SMEs mostly still lacks full functionality of e-commerce to serve various demands from customers over global marketplace promptly. This is a challenge for Thai SME exporters to improve their competence with e-commerce adoption in order to gain the competitive advantage.

An Enhanced K-Means and ANOVA-Based Clustering Approach for Similarity Aggregation in Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have recently been proposed as a way to observe and explore aquatic environments. Sensors in such networks are used to perform pollution monitoring, disaster prevention, or assisted navigation and to send monitored data to the sink. Compared with the traditional sensor networks, sensors in UWSNs consume more energy due to the acoustic technology used in under water communications. Node clustering is a common method to organize data traffic and reduce in-network communications while improving scalability and energy consumption. In this paper, we present a new clustering method to handle the spatial similarity between node readings. We suppose that readings are sent periodically from sensor nodes to their appropriate cluster heads (CHs). Then, a two-tier data aggregation technique is proposed. At the first level, each node periodically cleans its readings in order to eliminate redundancies before sending its data set to its CH. Once the CH receives all data sets, it applies an enhanced K-means algorithm based on a one-way ANOVA model to identify nodes generating identical data sets and to aggregate these sets before sending them to the sink. Our proposed approach is validated via experiments on real sensor data and comparison with other existing clustering and data aggregation techniques.

Residual energy-based adaptive data collection approach for periodic sensor networks

Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.

Some ecological side-effects of chemical and physical bush clearing in a southern African rangeland ecosystem

Bush thickening is a major concern to farmers of arid and semi-arid rangelands; reactive intervention remains the norm. Here we compared some of the short-term ecological implications of chemical and physical removal of the bush encroacher Acacia mellifera in the central Highland savanna of Namibia. We selected 21 invaded sites, 7 had been chemically cleared, 7 had been physically cleared and 7 had never been cleared. From each site, we recorded grass species composition, as well as the densities of A. mellifera, the undesired perennial shrub Pechuel-loeschea leubnitziae and a non-targeted tree Acacia erioloba, 24–30months post-treatment. We fitted one-way ANOVA models to test for the effect of treatment on grass species richness and density of targeted and non-targeted tree species. A canonical correspondence analysis was used to determine how treatment affected grass species composition. While both treatments reduced the density of the bush encroacher, P. leubnitziae effectively replaced A. mellifera in chemically treated sites, where die-back presumably happened faster and nutrient and water competition decreased more rapidly. In such sites, perennial grass species were effectively outcompeted by P. leubnitiziae.

P0132 A cross-sectional analytical study of the quality of life of Iranian cancer patients

Background Many patients with cancer have multiple symptoms that affect their sense of well-being and this has become an increasingly prominent topic in oncology research. The purpose of this study was to evaluate quality of life (QOL) in Iranian cancer patients undergoing chemotherapy. Methods 118 cancer patients who underwent chemotherapy and who met the inclusion criteria were enrolled in the study. This cross-sectional analytical study included consecutive Iranian patients treated at Qom Chemotherapy Center from January 2012, to June 2013. The participants completed an Iranian version of the brief form of the WHOQOL assessment instrument and a demographic checklist. Data were analysed using SPSS-PC software, descriptive analysis, independent sample t-tests, one-way ANOVA models, Tukey’s post hoc test, and linear regression. Findings The mean age of patients was 52.11 ± 1.57 years. Most were married (84%). 32% were housewives and 27% were unemployed. 36% were uneducated, 53% had an educational level of less than a diploma, and only 8% received academic education. The mean time from cancer diagnosis was 16. 76 ± 2.32 months. As for the site of cancer, the largest proportion had breast cancer (35%), followed by bowel (17%) and lung (10%) cancers. Most patients had a relatively average level of overall QOL. QOL scores were low in older and less educated patients but these scores were not affected by the marital status or type of cancer. No significant regression was observed between duration of disease and treatment and mean QOL scores. Interpretation The results emphasise that older and less educated patients have lower QOL scores. These patients may require a greater level of support and education to improve their QOL. Overall, patients in the present study had a relatively average level of global QOL.

TU-AB-BRA-06: Texture Feature Reproducibility Between PET/CT and PET/MR Imaging Modalities

Purpose: Radiomics involves the extraction of texture features from different imaging modalities with the purpose of developing models to predict patient treatment outcomes. The purpose of this study is to investigate texture feature reproducibility across [18F]FDG PET/CT and [18F]FDG PET/MR imaging in patients with primary malignancies. Methods: Twenty five prospective patients with solid tumors underwent clinical [18F]FDG PET/CT scan followed by [18F]FDG PET/MR scans. In all patients the lesions were identified using nuclear medicine reports. The images were co-registered and segmented using an in-house auto-segmentation method. Fifty features, based on the intensity histogram, second and high order matrices, were extracted from the segmented regions from both image data sets. One-way random-effects ANOVA model of the intra-class correlation coefficient (ICC) was used to establish texture feature correlations between both data sets. Results: Fifty features were classified based on their ICC values, which were found in the range from 0.1 to 0.86, in three categories: high, intermediate, and low. Ten features extracted from second and high-order matrices showed large ICC ≥ 0.70. Seventeen features presented intermediate 0.5 ≤ ICC ≤ 0.65 and the remaining twenty three presented low ICC ≤ 0.45. Conclusion: Features with large ICC values could be reliable candidates for quantification as they lead to similar results from both imaging modalities. Features with small ICC indicates a lack of correlation. Therefore, the use of these features as a quantitative measure will lead to different assessments of the same lesion depending on the imaging modality from where they are extracted. This study shows the importance of the need for further investigation and standardization of features across multiple imaging modalities.

Robust pairwise multiple comparisons under short-tailed symmetric distributions

In one-way ANOVA, most of the pairwise multiple comparison procedures depend on normality assumption of errors. In practice, errors have non-normal distributions so frequently. Therefore, it is very important to develop robust estimators of location and the associated variance under non-normality. In this paper, we consider the estimation of one-way ANOVA model parameters to make pairwise multiple comparisons under short-tailed symmetric (STS) distribution. The classical least squares method is neither efficient nor robust and maximum likelihood estimation technique is problematic in this situation. Modified maximum likelihood (MML) estimation technique gives the opportunity to estimate model parameters in closed forms under non-normal distributions. Hence, the use of MML estimators in the test statistic is proposed for pairwise multiple comparisons under STS distribution. The efficiency and power comparisons of the test statistic based on sample mean, trimmed mean, wave and MML estimators are given and the robustness of the test obtained using these estimators under plausible alternatives and inlier model are examined. It is demonstrated that the test statistic based on MML estimators is efficient and robust and the corresponding test is more powerful and having smallest Type I error.

Relationship between clinicopathologic characteristics and prognosis of patients with lymph node metastasis of primary liver cancer

Objective To investigate the clinicopathologic characteristics and prognosis of patients with lymph node metastasis of primary liver cancer. Methods The clinical data of 236 patients who underwent radical resection of primary liver cancer at the First Affiliated Hospital of Medical College of Xi'an Jiaotong University from August 2000 to August 2011 were retrospective analyzed. The clinicopathologic factors affecting survival time of patients with liver cancer, gender, age, pathological classification, histological grade, cirrhosis, alanine transami nase (ALT) , aspartate transaminase (AST) , alkaline phosphatase (ALP) , glutamyl-transpeptidase (GGT) , alpha-fetoprotein (AFP) , hepatitis, hepatic lobe distribution, tumor diameter, gross type, tumor number, satellite lesions, capsule integrity, liver capsule local invasion, vascular thrombosis, TNM stage and lymph node metastasis were analyzed. The patients received serum AFP and B ultrasound reexaminations every 3 months after operation and chest X-ray film every 6 months. The recurrence of liver cancer in suspected patients were confirmed by abdomen CT scan and/ or digital subtraction angiography (DSA) . The ultrasound-guided puncture histopatho-logical examination was done if necessary. The follow-up was done till 30, December 2013 or death. The survival time was calculated from operation date to death or the end of follow-up. The measurement data with normal distribution were presented as ±s and analyzed usingt test, and the count data were analyzed using chi-square test or Fisher exact probability. The survival curve was drawn by Kaplan Meier method, and the survival rate was analyzed using the Log rank test. The univariate analysis and multivariate analysis were done using the one-way ANOVA and COX regression model. Results Of 236 patients with primary liver cancer, there were 23 patients with lymph node metastasis and 213 patients without lymph node metastasis. The incidences of lymph node metastasis in patients with hepatocellular carcinoma, cholangiocarcinoma and hepatocellular cholangiocarcinoma were 7. 93%(13/ 164) , 15. 00% (9/ 60) and 8. 33% (1/ 12) , respectively. The lymph node metastasis which located in hepa-toduodenal ligament, hepatic pedicle, pancreatic and multiple locations were in 10, 6, 4 and 3 patients, respectively. Univariate analysis showed that the pathological classification, level of ALP, hepatitis, TNM stage, lymph node metastasis were risk factors affecting the prognosis of patients (F =3. 386, 4. 064, 2. 857, 22. 988, 4. 087, P 120 U/ L, TNM stage Ⅲ and positive lymph node metastasis were independent risk factors affecting the prognosis of patients (HR = 1. 533, 1. 592, 2. 032, 4. 086, 95% confidence interval: 1. 008-2. 331, 1. 019-2. 489, 1. 214-3. 399, 1. 996-8. 363, P 0.05) . Conclusions The hepatocellular carcinoma, ALP >120 U/ L, TNM stage Ⅲ and positive lymph node metastasis are independent risk factors affecting the prognosis of patients with primary liver cancer, and lymph node metastasis is closely related to liver lobe distribution of tumor, satellite lesions and partial liver capsule invasion. There is different in the prognosis of patients with positive lymph node metastasis and different pathological classification of primary liver cancer. Key words: Liver neoplasms; Pathology, clinical; Lymph node metastasis; Prognosis

A longitudinal analysis of the effects of open enrollment on equity and academic achievement: Evidence from Minneapolis, Minnesota

Open enrollment was expected to provide students in urban school settings with equal opportunity to access schools with abundant educational resources that led to improved student achievement. The One-way ANOVA and Linear Mixed Models used a propensity score matching method were administered to identify to what extent urban students utilized inter-district open enrollment in a Midwestern city and to compare their performances on standardized tests before and after the school transfer had occurred. The results indicated that open enrollment provided black students and students in the child welfare system with equal access to racially and socioeconomically integrated schools. However, these students' academic performance was not significantly enhanced by their open enrollment, except the 3rd grade student achievement in math. The results raised questions about the characteristics of open enrollment. Recommendations for future research are made; study limitations are addressed.

Predictors of a successful medical weight loss program

BackgroundMany practices are creating weight loss programs, in preparation for bariatric surgery or for patients who wish to lose weight without surgery. Preoperative weight loss may be associated with improved postoperative weight loss and resolution of co-morbidities. The aim of this study is to investigate the success of a preoperative weight loss program at a single institution and the variables associated with success in weight loss. MethodsWe enrolled patients in a once monthly multidisciplinary preoperative weight loss program and evaluated % total weight lost over the 6-month program for primary and for revisional bariatric surgical patients. Demographic characteristics, weight, program related factors, and co-morbidities were recorded. One-way ANOVA and multiple linear regression models were carried out to assess variables. Parameter estimates of multiple linear regression models were reported. Statistical significance was set at .05 and analysis was done using SAS 9.3. ResultsA total of 133 patients enrolled and completed the program over a period of 14 months. Only 50.8% of the patients lost weight with average weight loss of .1±4.0 lbs. Patient׳s sex, insurance, psychiatric history, co-morbidities, referral status, or type of counseling had no significant effect on weight loss (P>.05). Patients between 30 and 50 years old on average were more successful in losing weight (P = .018). Patients considering revisional surgery were less successful preoperatively compared to first time candidates (P = .0007). ConclusionPatients between 30 and 50 years of age, first time surgical candidates, and those with higher weights may be more successful in losing weight in a preoperative bariatric weight loss program.

Impaired cognition in depression and Alzheimer (AD): a gradient from depression to depression in AD.

To assess cognition in major depressed (MD), Alzheimer's disease (AD), and depression in AD elderly. Subjects were evaluated by Mini Mental, Rey Auditory Verbal Learning Test, Rey Complex Figure, Digit Span, Similarities, Trail Making A/B, Verbal Fluency and Stroop. One-way ANOVA and multivariate models were used to compare the performance of each group on neuropsychological tests. We evaluated 212 subjects. Compared to MD, attention, working memory, processing speed and recall showed significantly better in controls. Controls showed significantly higher performance in all cognitive measures, except in attention compared to AD. Verbal fluency, memory, processing speed and abstract reasoning in MD was significantly higher compared to AD. AD was significantly better in general cognitive state than depression in AD. All other cognitive domains were similar. A decreasing gradient in cognition appeared from the control to depression in AD, with MD and AD in an intermediate position.

Dose–Response Modeling Under Simple Order Restrictions Using Bayesian Variable Selection Methods

Bayesian modeling of dose–response data offers the possibility to establish the relationship between a clinical or a genomic response and increasing doses of a therapeutic compound and to determine the nature of the relationship wherever it exists. In this article, we focus on an order-restricted one-way ANOVA model which can be used to test the null hypothesis of no dose effect against an ordered alternative. Within the framework of the dose–response modeling, a model uncertainty can be addressed using model averaging techniques. In this setting, the uncertainty is related to the number of all possible models that can be fitted to the data and should be taken into account for both inference and estimation. In this article, we propose an order-restricted Bayesian variable selection model that addresses the model uncertainty and can be used for both inference and estimation. The proposed method is applied to two case studies and is compared to the likelihood ratio test and the multiple contrast tests in both the analyses of the case studies and a simulation study. This article has online supplementary material.

Hepatic 11β-hydroxysteroid dehydrogenase type 1 activity in obesity and type 2 diabetes using a novel triple tracer cortisol technique.

Dysregulation of 11β-hydroxysteroid dehydrogenase (11β-HSD) enzyme activities are implicated in the pathogenesis of obesity and insulin resistance. The aim of the study was to determine whether hepatic 11β-HSD type 1 (11β-HSD-1) enzyme activity differs in people with and without obesity and type 2 diabetes. We measured hepatic 11β-HSD-1 activity in the overnight fasted state in 20 lean non-diabetic participants (LND), 21 overweight/obese non-diabetic participants (OND) and 20 overweight/obese participants with type 2 diabetes (ODM) using a non-invasive approach. One mg doses of [9,12,12-(2)H3]cortisol (D cortisol) and [4-(13)C]cortisone ([(13)C]cortisone) were ingested, while [1,2,6,7-(3)H]cortisol ([(3)H] cortisol) was infused intravenously to enable concurrent measurements of first-pass hepatic extraction of ingested D cortisol and hepatic conversion of ingested [(13)C]cortisone to C13 cortisol derived from the ingested cortisone (a measure of 11β-HSD-1 activity in the liver) using an isotope dilution technique. One-way ANOVA models and Kruskal-Wallis tests were used to test the hypothesis. Plasma D cortisol and C13 cortisol concentrations were lower in OND than in LND (p < 0.05) over 6 h of the study. There was no difference (p = 0.15) in C13 and D cortisol concentrations between OND and ODM and between LND and ODM for the same study period. Hepatic conversion of [(13)C]cortisone to C13 cortisol was similar between groups. Hepatic conversion of [(13)C]cortisone to C13 cortisol did not differ between the groups studied. We conclude that hepatic 11β-HSD-1 activity is similar in individuals who are overweight/obese or who have type 2 diabetes.

Primary care nurses’ awareness of and willingness to perform children’s oral health care

BackgroundThe majority of young children receive no early dental examination while attending primary health care for routine check-ups. Our aim was to study primary care nurses’ knowledge of oral health care (OHC) and their attitudes toward delivering OHC, as well as to assess their willingness to obtain OHC information.MethodsWe conducted a cross-sectional survey of all primary-care nurses working in the public health centres of Tehran city. An anonymous self-administered questionnaire queried their knowledge in paediatric-, general and medicine-related areas of dentistry, providing knowledge scores for three domains. The nurses’ attitudes toward OHC and their willingness to obtain OHC information underwent evaluation with statements utilizing a five-point Likert scale. Altogether 680 nurses took part in the survey. The Chi-square test, t-test, one-way ANOVA and logistic regression model served for statistical analyses.ResultThe mean score for the paediatric dentistry domain (3.6, SD: 1.5) was lower than for the medical (4.4, SD: 2.3) and dental domains (5.8, SD: 1.5). Obtaining higher scores in the paediatric (OR = 1.2) and dental (OR = 1.3) domains, and a greater willingness to receive OHC information (OR = 5.3), were associated with a positive attitude toward OHC. Nurses with a lower education (OR = 1.9) and better oral health behaviour (OR = 1.1) as well as those working in a non-affluent region (OR = 1.6) had a more positive attitude toward OHC.ConclusionPrimary care nurses’ low level of knowledge in OHC and their positive attitude and willingness to obtain more information point to the need for appropriate OHC training and encouragement for the nurses to promote oral health and prevent dental diseases.

Inference for one-way ANOVA with equicorrelation error structure.

We consider inferences in a one-way ANOVA model with equicorrelation error structures. Hypotheses of the equality of the means are discussed. A generalized F-test has been proposed by in the literature to compare the means of all populations. However, they did not discuss the performance of that test. We propose two methods, a generalized pivotal quantities-based method and a parametric bootstrap method, to test the hypotheses of equality of the means. We compare the empirical performance of the proposed tests with the generalized F-test. It can be seen from the simulation results that the generalized F-test does not perform well in terms of Type I error rate, and the proposed tests perform much better. We also provide corresponding simultaneous confidence intervals for all pair-wise differences of the means, whose coverage probabilities are close to the confidence level.

Study on Cognition, Attitude and Behavioral Path of Energy Saving and Carbon Reduction: Using University Students in Taiwan as Examples

By questionnaire survey, this study probes into university studentscurrent cognition and attitude toward energy saving and carbon reduction and conducts path analysis. The research tool is self-designed questionnaire on cognition and attitude toward energy saving and carbon reduction. First, 9 experts and scholars are invited to test content validity of questionnaires and conduct questionnaire pretest. The researcher revises the questionnaire, practices questionnaire survey, analyzes data and selects 685 university students by stratified cluster sampling. There are 617 valid questionnaires. Data obtained are analyzed by descriptive statistics, independent sample t test, One-way ANOVA and structural equation model.

A systematic review on marginal bone loss around short dental implants (&lt;10 mm) for implant‐supported fixed prostheses

This systematic review aimed to evaluate the effect of implant length on peri-implant marginal bone loss (MBL) and its associated influencing factors. An electronic search of the PubMed and MEDLINE databases for relevant studies published in English from November 2006 to July 2012 was performed by one examiner (AM). Selected studies were randomized clinical trials, human experimental clinical trials or prospective studies (e.g., cohort as well as case series) with a clear aim of investigating marginal bone loss of short dental implants (<10 mm) supporting fixed prostheses. A random-effect meta-regression model was used to determine the relationship between the effect size mean MBL and the covariate "implant length." Additionally, a subgroup analysis, by means of a random-effect one-way ANOVA model, comparing mean MBL values at different levels of each factor ("type of connection" and "type of prostheses") was also performed. The meta-regression of mean MBL on the moderator "implant length" was found to be insignificant (P = 0.633). Therefore, it could not be concluded that implant length had an effect on peri-implant MBL. In addition, standardized differences in mean MBL on the subgroups short (<10 mm) and standard (≥ 10 mm) implants, as determined by the meta-analysis (random-effect model), were found to be statistically insignificant (P = 0.222). Within limitations of the present systematic review, it could be concluded that short dental implants (<10 mm) had similar peri-implant MBL as standard implants (≥ 10 mm) for implant-supported fixed prostheses.