Statistical Analysis. 31 F-tests and t-tests will be used to analyze OV and Quality Acceptance data. The F-test is a 32 comparison of variances to determine if the OV and Quality Acceptance population variances 33 are equal. The t-test is a comparison of means to determine if the OV and Quality Acceptance 34 population means are equal. In addition to these two types of analyses, independent verification 35 and observation verification will also be used to validate the Quality Acceptance test results.
Statistical Analysis. Behavioral Objectives: In order to attain this competency, the student should be able to: Perform a laboratory that applies statistical methods to the analysis of experimental data, real or simulated (This competency is recommended by the ACS but not required as part of this agreement).
Statistical Analysis. The research questions of interested examined the relationship between the two area- level exposures, area disadvantage and local racial/ethnic spatial concentration, and HIV testing. We describe the study population by their individual- and ZCTA-level characteristics by area disadvantage and local racial/ethnic spatial concentration and present the prevalence of ever having tested for HIV stratified by the disadvantage and ICE quintiles for the three race/ethnic groups. For the multivariable analysis, we conducted multilevel modeling with a modified Poisson approach (111) to estimate prevalence ratios and used generalized estimating equations with an exchangeable correlation structure to account for clustering by ZCTA. Subjects with missing data for the outcome and/or covariates were excluded from the analyses. Area disadvantage and ICE and their relationships with the outcome were examined separately. Our modeling strategy fit a series of models: (i) unadjusted without interaction, (ii) exposure X race interaction-only model, (iii) fully adjusted model adjusting for individual- and area-level covariates with exposure x race interaction. For the models with interaction, we assessed multiplicative interaction with the generalized score test statistic and its p-value and additive interaction with the relative excess risk due to interaction (RERI) from the relative risk model (112). A RERI calculated using risk ratios (RERIRR) indicates the direction of the additive interaction, whether positive or negative, but not the relative magnitude of the interaction. To address confounding, we adjusted for the following individual- and area-level covariates based on prior literature (35,41,97,113): age in years; sexual identity; educational status; sexual identity disclosure; health insurance; health care provider visit in the past one year; CAI; STI testing and diagnosis in the past year; having ever tested for HIV; having heard of PrEP; urban- rural residence; and region of the country. Multicollinearity was assessed by examining condition indices (greater than 35) and variance decomposition factors (two or more greater than 0.5). Since these criteria were not met, all covariates were retained in the final model. The findings presented focus on the comparisons between the extremes - the most (Q5) and least (Q1) disadvantaged ZCTAs and the highest POC concentration (Q5) and highest White concentration ZCTAs (Q1). We did not include Asian and Pacific Islander partici...
Statistical Analysis. All of the statistical analyses were performed with dedicated software (Stata Statistical version 12; StataCorp, College Station, TX, USA). The percentage of sectors with residual cancer was calculated for each score, and Xxxxxxxx correlation analysis was performed to assess the relationship between percentage and PI-RR score. The per-sector sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the detection of the sector with residual cancer were calculated at PI-RR category 3 and 4 thresholds. To determine sensitivity, the cancer detection rate (CDR) per sector was calculated as the ratio of the number of sectors with suspicious MRI findings ultimately confirmed to be PCa to the total number of sectors with residual lesions found on histology; this was A B C D E T2WI DWI ADC DCE PI-RR category PI-RR 1 PI-RR 2 PI-RR 3 PI-RR 4 PI-RR 5
Statistical Analysis. DHS agrees to integrate student data into its existing data warehouse and generate analytical reports that provide the distributions of students receiving DHS services across the District. The analytical reports shall be de-identified aggregate reports. DHS shall identify attributes and indicators for academic and behavioral successes and challenges. Critical Reflection. DHS shall present the analysis to all parties and together engage in careful examination of the data in an effort to develop effective strategies for improving both organizations’ ways of working with children and families. Action. DHS shall create, implement, and assess strategies developed through the statistical analysis and critical reflection phases. DHS shall work with the District to implement these strategies in schools and in the community.
Statistical Analysis. Standard statistical methods were used to calculate the means, standard deviations, and absolute and relative frequencies. The Kolmogorov-Smirnov and Xxxxxx tests were used to assess the normality and homogeneity of the distributions respectively; data were analysed using parametric or non-parametric tests according to the results. A Xxxx- Xxxxxxx test or unpaired t-test was used to evaluate the differences between the Control and Panel Qualification and Final scores respectively. The reliability between E/A C&P-scores was calculated using a one way-random, absolute agreement Intraclass Coefficient of Correlation (ICC), because each routine was rated by judges randomly selected from a larger population of judges. ICC values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability respectively (Xxx & Xx, 2016). Validity was assessed by comparing the concrete judging (E/A P-scores) with the gold standard score (E/A C-Score). Systematic over- or under-rating of scoring, also known as bias (Xxxxx et al., 2012) was also investigated as a further step in the analysis. The Execution/Artistic differences were computed as the differences between the two human scoring systems, which indicated bias, i.e. systematic under- or over-estimation of Execution/Artistic scores. These differences were defined as: E/A C-P bias = E/A C-Score – E/A P-scores. If the gold standard method (E/A C-Score) is sometimes higher, and sometimes the other method (E/A P-Score) is higher, the average of the differences will be close to zero. If it is not close to zero, this indicates that the two assay methods are systematically producing different results. Assuming that the E/A C-Scores, given by the highest category of judges, are accurate, the concordance value would show to what extent the scores assigned by the panel are correct. Xxxxx-Xxxxxx plots (Xxxxx & Xxxxxx, 1986) were used to assess and display agreement along the entire spectrum of scores and at each Qualitative Performance Range in Qualification and Final competition. Systematic C-P scores bias and the 95% limits of agreement (LoA = C-P scores bias ±1.96 SD) were calculated. Each Xxxxx-Xxxxxx plot shows the limits of agreement (LoA), calculated by using the mean ±2 standard deviations of the differences between the two E/A C-P scores. The difference of the two paired measurements is plotted against the mean of the same two measurements, an...
Statistical Analysis. A nested case-control setup in the XXXXXX trial was used. This design uses a selection of the control subjects to represent all control subjects in the full cohort, enabling reconstruction of the results for the full cohort (10). For that purpose, the results for the control group are multiplied by the quotient of one divided by the sample fraction. The cases of cancer in which no chest radiograph was available (10 [15.4%] of 65) were excluded. To determine the sample frac- tion, the number of noncases, therefore, had to be adjusted accordingly (4873 2 15.4% = 4123) to match the 1.3% can- cer rate in the XXXXXX cohort at the time of this study. The sample fraction in our study, therefore, was 72/4123. We tested whether the control sub- jects in the observer study were rep- resentative of all noncases in the full cohort (a requirement of a nested case- control study). Categorical variables were evaluated by using a x2 test, and continuous variables were evaluated with a Student t test. In all calculations, we assumed 100% sensitivity for CT. Confidence in- tervals (CIs) were calculated by using the Xxxxxx score. The following four parameters were used to assess the performance of chest radiography as a screening tool for lung cancer:
Statistical Analysis. After analyzing price premiums descriptively, we estimated six regression models. The dependent variable (i.e., the relation between a pharmaceutical’s costs and those of its comparator) appeared to be gamma-distributed (many values at or close to 0), which we confirmed by applying the Modified Park Test [43]. Therefore, we could not use a simple ordinary least squares (OLS) regression. To avoid retransformation problems we preferred a generalized linear model (GLM) over a log OLS model [43]. Applying the Pregibon Goodness of Link Test [44] we confirmed the usage of a log-link. Thus, we used a GLM with a log-link function to analyze the impact of added benefit on price pre- miums. To allow for meaningful interpretation, we calculated marginal effects for each variable, with all other variables set to their means. We conducted several sensitivity analyses. First, because single observations may have a large impact on results based on such a small sample, we excluded substances with a Xxxx’x D greater than the conventional cutoff point of 4/n [45]. Second, we controlled for whether pharmaceuticals had been assessed dur- ing the first 7 months after the AMNOG legislation came into effect. During this transitional period, manufacturers were advised on the completeness of their dossiers by the G-BA and, if required, were granted an additional 3 months to complete them [46]. Third, we included a variable that controlled for whether the final price had been set by the arbitration board. Last, instead of using average comparator costs when several interchangeable comparators were eligible for the same patient subgroup, we reran our models using the least and the most costly comparators. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC).
Statistical Analysis. All data are expressed as mean ± SEM. Comparisons between NPC-FF, and FF grafted animals were made by nonpara- metric Xxxx-Xxxxxxx X tests and P<0.05 was considered significant.
Statistical Analysis. For the agreement between the 2 cytology raters, we calcu- lated the total agreement with a binomial 95% confidence interval (95% CI). We calculated the Xxxxx kappa with 95% CI as a chance-corrected measure of agreement as described by Xxxxxxx.18 Because kappa does not account for the degree of disagreement between categories and treats any disagreement equally, we calculated linear- weighted kappa with 95% CI for the ordered cytology cat- egories. Thus, disagreement between adjacent categories results in a lower reduction of kappa values than disagree- ment between nonadjacent categories. Kappa values < 0.20 were interpreted as poor, values between 0.21 and 0.40 were interpreted as fair, values between 0.41 and 0.60 were interpreted as moderate, values between 0.61 and 0.80 were interpreted as good, and values > 0.80 were interpreted as very good. Exact versions of symmetry (4-category) and XxXxxxx (2-category) chi-square tests were used to test for statistically significant differences in the distribution of the cytologic interpretations between raters. A nonparametric test of trend was used to assess the trend in the percentage of positive results for each bio- marker for the risk of AIN2 or higher (AIN2+) with increasing severity of the cytologic interpretation.19 Finally, a Xxxxxx exact test was used to test for differences in the percentage of positive results for each biomarker between subgroups defined by the paired cytologic interpretations.
