Statistical Analyses. Author Manuscript To make variables more clinically sensible for decision making in the ED, all patient history and physical examination variables were assessed as dichotomous (ie, presence or absence of a finding). We determined the interobserver agreement for each clinical finding by calculating the unweighted Xxxxx kappa (κ) statistic with two-sided 95% confidence intervals (CIs) as well as the percent agreement. The interobserver agreement was categorized based on the κ point estimates as slight (0–0.2), fair (0.21–0.4), moderate (0.41– 0.6), substantial (0.61–8), and almost perfect (0.81–1.0). (25,26) For each variable, we excluded from the κ analysis any paired observations for which data were missing or at least one assessor marked the variable as “unsure.” Author Manuscript
Statistical Analyses. 2.8.1 Selection and missing values
Statistical Analyses. A statistical mixture model was used for the analysis of the harborwide stormwater outfall data. A detailed description of the model (including the key assumptions, limitations, and statistical output) is provided in Appendix C. The model was used to analyze the distribution of the geometric mean concentrations for harborwide outfall basins and to assign each outfall basin to one of three concentration groups (i.e., lower, moderate, or higher). This concept is consistent with the JSCS method for identifying low, medium, and high priority sites. Statistical analysis was performed for the analytes selected based on the data screening described in Section 4.2 (unless the number of samples was insufficient to conduct a valid statistical analysis; see Table 4-1). Graphs of the results of the statistical evaluation are included in Appendix C. Use of the full harborwide data set in the statistical evaluation allowed comparison of stormwater concentrations in City outfall basins to other stormwater discharges in the Portland Harbor Study Area. Source tracing categories were statistically identified based on specific outfall concentrations relative to harborwide concentration levels. For 5 See data tables in Appendices A and B for data collected by the City; see Anchor and Integral 2008b for City outfall basin data collected by others. each analyte included in the statistical evaluation, each outfall basin was assigned to one of three source tracing categories:
1. Stormwater analyte concentrations in these basins are lower relative to stormwater concentrations harborwide (i.e., relative to other private and public outfalls) and are least likely to have significant uncontrolled sources. The need for additional source tracing in City outfall basins within this category is low. Category 2. Stormwater analyte concentrations in these basins generally fall within a narrow moderate range and may or may not have significant uncontrolled sources. The need for additional source tracing in City outfall basins within this category should be evaluated further, unless otherwise indicated (see discussion below in Section 4.4). Category 3. Stormwater analyte concentrations in these basins are higher relative to harborwide concentrations and are the most likely to have significant uncontrolled sources. The need for additional source tracing in City outfall basins in this category is evaluated. The break lines defining Categories 1 through 3 for each analyte are described and sh...
Statistical Analyses. Categorize stormwater analytes in each basin by relative likelihood of having significant uncontrolled sources.
Statistical Analyses. During the sample size calculations, we anticipated a normal distribution of the arthritis scores. Nevertheless, upon visual inspection of the data, the standards required for parametric statistical testing were not met. Therefore, the non-parametric Xxxx-Xxxxxxx U test was applied for all statistical analyses to determine significance of observed differences. To determine whether TP administration had an effect on Pred treatment in the CAIA model and side effects of Pred, significant differences were only determined between the Veh-treated group and the Pred-treated group, with or without TP. Important to note is that, with this study, we want to find out whether analgesia can be applied and would provide us with a similar extent in parameters required to evaluate whether alternatives to Pred are superior. Therefore, no analysis has been performed between the non-TP and TP-treated groups, except when the effect of TP on GR target-gene expression was evaluated. All statistical analyses were performed using GraphPad Prism 9. Notion should be taken when interpreting the p-values since the Veh + TP-treated group included only 3 mice and therefore comparisons to this group cannot reach statistical significance.
Statistical Analyses. Statistical analyses of the registry data will be performed in regular intervals. For qualitative parameters, descriptive statistics such as the population size and the percentage of available data for each class of the parameter will be presented. Quantitative parameters will be summarized by presenting, for example, the population, the mean, standard deviation (SD), median, minimum and maximum values, or interquartile ranges. Statistics may be presented, if sample size permits, for cohorts of interest. Due to the observational nature of the registry, all analyses will be considered exploratory. All patients entered in CARE are intended to be included in the analyses. Patients with missing data will not be excluded from the patient analysis population, but will be included to the extent that evaluable data are present. However, some patients with missing values may be excluded from specific analyses.
Statistical Analyses. The agreement for nominal variables on routes to diagnosis was assessed by chance-corrected agreement in the form of kappa coefficients [17] and Gwet’s AC1 [18]. Xxxxx’x kappa statistics is widely used to compute agreement between raters on nominally scaled data. However, it is known to be affected by an unbalanced prevalence of the trait, i.e. in a situation where a large proportion of ratings is either positive or negative, kappa may then yield a low value despite high overall percentage agreement [19]. We used AC1 as an alternative agreement coefficient to remediate this issue. Agreement measured by kappa and AC1 was interpreted as: poor (below 0), slight (0–0.2), fair (0.2–0.4), moderate (0.4–0.6), substantial (0.6-0.8) and
Statistical Analyses. A SAP detailing the analyses will be developed prior to database lock. All statistical analyses and data summaries will be performed using SAS® (Version 9.1 or higher) or other validated software. The SAP will serve as the final arbiter of all statistical analyses. Data will be summarized overall using descriptive statistics. Continuous data will be summarized with number of patients (n), mean, median, minimum, maximum, relevant quartiles, standard deviation, coefficient of variation, and geometric mean (where applicable). Categorical data will be summarized using frequency counts and percentages.
Statistical Analyses. The concordance rates for responses to the clinical questions raised by the referring physicians, clinically pertinent findings, incidental findings, and proposed investigations were tabulated to in 88.1% of cases (95% CI, 80.6%–92.9%). The Xxxxx n for pos- itive studies was 0.86 (0.77– 0.95) and 0.59 (0.45– 0.74) for the presence/absence of any clinically pertinent findings and for inci- dental findings, respectively. Readings were in agreement for 75.6% (67.2%– 82.5%), 65.5% (56.6%–73.5%), and 86.6% (79.2%–91.6%) of clinically pertinent findings, incidental findings, and recommendations for further in- vestigations, respectively (Table 1). Rates were similar for the 2 sub- groups (referred from the neuro-oncology clinic or from all other clinics). Class 2 discrepancies in reporting clinically pertinent findings were found in 18 cases (15.1%). They are summarized in Table 2. Examples include the presence or absence of a tumor recurrence FIG 2. Class 2 discrepancies in incidental findings. A, One reader reported a polypoid posterior nasal lesion (arrow), whereas the other observer did not mention this incidental finding. B, A pineal cyst (circle) was mentioned in only 1 of the 2 reports. independent observers. Their study showed agreement in 51%, 61%, and 74% of abdominal, chest, and skeletal x-rays, respectively. They also assessed performance by calculating n statistics of interobserver agreement. Weighted n values between pairs of observers were higher with skeletal (0.76 – 0.77) than with chest (0.63– 0.68) or abdominal (0.50 – 0.78) examinations. In a meta- analysis conducted by Xx et al,2 the global discrepancy rate was 7.7% (in- cluding a major discrepancy rate of 2.4%). The major discrepancy rate var- ied according to body region: It was lower for head (0.8%) and spine CT (0.7%) than chest (2.8%) and abdomi- nal CT (2.6%). Blinding of the reference (n = 4, Fig 1A), the growth of a meningioma (n = 2), the evolu- tion of chronic subdural hematomas (n = 2), and the presence of a lytic bone lesion (Fig 1B). These discrepancies were normally distributed between readers (n = 1, 3, 5, 7, 11, 5, 3, 1 for 36 discrepant reports). There was no significant difference between contrast-enhanced (n = 9 of 53) and nonenhanced studies (n = 9 of 66, P = .62). Class 1 discrepancies in clinically pertinent findings were seen in the interpretation of 11 cases. Examples include the location of recent ischemic lesions (n = 2), tumor extensions (n = 2), or the disconnection...
Statistical Analyses. The multirater n statistics were computed by using the macro MAGREE with SAS, Version 9.3 (SAS Institute, Xxxx, North Car- olina). This macro implements the methodology of Fleiss et al,27 measuring the agreement when the number of raters is >2. This method also allowed identifying, for each scale, the categories in Ob1 0.497 ± 0.098 0.411 ± 0.102 0.478 ± 0.103 0.544 ± 0.101 0.508 ± 0.104 0.315 ± 0.114 0.517 ± 0.100 0.580 ± 0.100 0.519 ± 0.094 Ob2 0.419 ± 0.100 0.286 ± 0.102 0.576 ± 0.096 0.506 ± 0.102 0.320 ± 0.115 0.458 ± 0.096 0.538 ± 0.099 0.330 ± 0.089 0.330 ± 0.089 Ob3 0.197 ± 0.088 0.284 ± 0.100 0.513 ± 0.103 0.191 ± 0.096 0.339 ± 0.103 0.345 ± 0.105 0.404 ± 0.103 Ob4 0.510 ± 0.100 0.343 ± 0.103 0.352 ± 0.103 0.384 ± 0.098 0.583 ± 0.098 0.297 ± 0.087 Ob5 0.602 ± 0.101 0.594 ± 0.102 0.712 ± 0.091 0.752 ± 0.082 0.397 ± 0.094 Ob6 0.525 ± 0.107 0.465 ± 0.108 0.610 ± 0.101 0.425 ± 0.093 Ob6a 0.542 ± 0.096 0.421 ± 0.106 0.283 ± 0.085 Ob7 0.511 ± 0.102 0.442 ± 0.102 Ob8 0.423 ± 0.095 All observers n = 0.44570 ± 0.013176; P < 0.001 Note:—Inter-observer Kappa values S 0.6 are highlighted in bold type.
