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. 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 disconnecti...
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 Table 1: Interobserver agreement using the TICI reperfusion scale Ob2 Ob3 Ob4 Ob5 Ob6 Ob6a Ob7 Ob8 Ob9 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 ≥ 0.6 are highlighted in bold type.
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. 2.8.1 Selection and missing values We excluded test and/or retest patient questionnaires if they had been completed >30 days post-consultation, and physician questionnaires if they had been completed >7 days post-consultation (Figure 1). We assumed that a longer period would be detrimental to participants’ recollection of the decision-making process. We handled missing values according to authors’ recommendations, if provided in the original or Dutch validation paper (see section 2.5).12, 13, 34 For the other questionnaires and the iSHARE questionnaires (see section 2.4), we only report scores when all respective items had been completed. We report sample sizes per analysis, since these may differ due to missing values.
Statistical Analyses. Categorize stormwater analytes in each basin by relative likelihood of having significant uncontrolled sources.
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:
Statistical Analyses. We first described the study population by presenting their demographic, socioeconomic, and health-related characteristics by their disclosure status and residence. To examine the exposure and outcome associations, we conducted multilevel, multivariable modeling using generalized estimating equations (GEE) with a binomial distribution and logit link and an exchangeable correlation structure, which accounted for clustering by ZCTA. Subjects with missing data for the outcome and/or covariates were excluded from the analyses. Our modeling strategy first examined the unadjusted association between exposure and outcome and then proceeded to examine interaction between urban-rural residence and perceived neighborhood tolerance perception on the multiplicative scale (interaction-only model). In the fully adjusted model, we adjusted for individual-level (age, race/ethnicity, education, sexual identity, and experienced sexual identity-related stigma) and ZCTA-level covariates (region, median household income, percent with less than a high school education, and percent of same-sex households) to address potential confounding. For the residence and disclosure to a HCP association, the models were adjusted for the same covariates, in addition to health insurance status, seeing a HCP in the past year, and sexual identity-related stigma in health care. These covariates were selected based on their associations with sexual identity disclosure, overall and to HCPs specifically, from previous literature (30,50,63,74,78,88,89). We also examined collinearity among the covariates, and the ZCTA-level percent of individuals with health insurance was not included in the models due to collinearity issues. Missing data and sensitivity analyses There were 10,023 participants who had disclosed their sexual identity, and 28% (2,787) did not respond to the subsequent question asking whether they had disclosed their sexual identity to a HCP, the second outcome of interest. We did not find any meaningful differences between those who responded and those who did not by age, education, or sexual identity. Those who were missing disclosure to HCP data had a higher proportion of missing for health insurance status (16% versus 10%) and having seen a health provider in the past year (13% versus 4%), compared to those who did not have missing data. There were 4,105 participants (41%) who were missing data on at least one covariate. We conducted sensitivity analyses to assess the impact of the ...
Statistical Analyses. The Microsoft Excel randomization function was used for randomly selected measurement values from one eye for each normal subject. All statistical analysis was performed by Stata 14.0 (Stata Corp., College Station, TX, USA). The data obtained from both devices were listed as the mean and standard deviations (SD) for continuous variables. The difference between different measurements was examined by using Xxxxx-Xxxxxx plots[18]. In addition, we described the variability of the measured values by calculating the coefficient of repeatability (COR=1.96-fold SD), relative COR Table 1 Comparison of CCT, corneal curvature and corneal astigmatism with pentacam and CASIA2 Variable Pentacam CASIA2 Mean difference P CCT (μm) 548.2±28.4 541.2±29.4 7.03±9.70 <0.001 Anterior Kf (D) 40.3±1.88 40.6±1.77 -0.27±0.35 <0.001 Anterior Ks (D) 42.2±1.94 42.4±1.96 -0.19±0.41 0.002 ACA (D) 1.93±1.12 1.89±1.04 0.04±0.47 0.429 Posterior Kf (D) -5.79±0.32 -5.68±0.28 -0.11±0.11 <0.001 Posterior Ks (D) -6.15±0.41 -5.98±0.38 -0.17±0.23 <0.001 PCA (D) 0.46±0.82 0.48±0.67 -0.02±1.02 0.846 TNP Kf (D) 39.1±1.83 39.6±1.71 -0.52±0.46 <0.001 TNP Ks (D) 41.0±1.81 41.4±1.90 -0.41±0.43 <0.001 TCA (D) 1.61±1.00 1.76±1.07 -0.15±0.96 0.188 CCT: Central corneal thickness; Ks: Steep corneal curvature; Kf: Flat corneal curvature; TCA: total corneal astigmatism; PCA: Posterior corneal astigmatism; ACA: Anterior corneal astigmatism; TNP: True net power. Table 2 Comparison of two measurements of CCT, corneal curvature and corneal astigmatism with Pentacam and CASIA2 Variable COR Relative COR, % LLOA ULOA LOA range R2 P CCT (μm) 19.0 0.04 -12.0 26.0 38.0 0.010 0.393 Xxxxxxxx Xx (D) 0.69 0.02 -0.96 0.43 1.39 0.037 0.107 Anterior Ks (D) 0.80 0.02 -1.00 0.61 1.61 0.010 0.414 ACA (D) 0.92 0.48 -0.88 0.97 1.85 0.032 0.131 Posterior Kf (D) 0.22 -0.04 -0.32 0.10 0.42 0.016 0.286 Posterior Ks (D) 0.45 -0.07 -0.62 0.28 0.90 0.009 0.418 PCA (D) 2.00 4.25 -2.02 1.98 4.00 0.037 0.105 TNP Kf (D) 0.90 0.02 -1.43 0.38 1.81 0.075 0.020 TNP Ks (D) 0.84 0.02 -1.24 0.43 1.67 0.052 0.054 TCA (D) 1.88 1.12 -2.03 1.73 3.76 0.008 0.977 COR: Coefficient of repeatability; ULOA: Upper limit of agreement; LLOA: Lower limit of agreement; CCT: Central corneal thickness; Ks: Steep corneal curvature; Kf: Flat corneal curvature; PCA: Posterior corneal astigmatism; ACA: Anterior corneal astigmatism; TNP: True net power; TCA: Total corneal astigmatism. (rCOR=COR/average measurement) and limits of agreement (LOA=mean±COR). The magnitude of ...
Statistical Analyses. The approaches and levels of response were analyzed descriptively. The intraobserver and interobserver agree- ments in level selection for LP were assessed by kappa sta- tistics. The kappa values were evaluated as follows: poor, <0.20; fair, between 0.21 and 0.40; moderate, between 0.41 and 0.60; good, between 0.61 and 0.80; and excel- lent, >0.80. The factors that were related to decisions to perform LP in each respondent were revealed by Student’s t-test, chi-square test, Xxxx-Xxxxxxx U-test, or Xxxxxx’x Asian Spine JoUrnal Table 1. Baseline characteristics of 30 survey cases N Sex Age (yr) Sx onset (mo) Motor weakness C2–C7 lordosisa) SKD Levels of compression MCS Levels of OPLL Foraminal stenosis 1 Female 54 9 Y 11.3 Y 3 2.00 0 2 2 Male 60 2 Y 15.9 N 5 1.80 5 0 3 Female 72 120 Y 0.6 Y 4 1.50 0 4 4 Male 68 14 N 1.4 N 5 1.80 5 1 5 Female 67 20 N –3.6 Y 4 1.25 3 3 6 Female 72 7 Y 29.2 N 3 1.67 2 0 7 Male 55 6 N 9.4 N 5 1.80 5 0 8 Female 62 2 Y 12.0 N 4 1.50 4 0 9 Female 48 8 Y –1.0 Y 3 1.67 1 2 10 Female 69 6 N –7.6 Y 4 2.25 0 1 11 Male 63 3 Y 23.7 N 2 2.00 0 2 12 Male 56 3 Y –4.9 Y 4 1.25 0 0 13 Female 55 6 Y –4.7 Y 2 1.50 0 1 14 Female 78 5 Y –7.9 N 5 1.20 0 2 15 Female 65 1 N 0.6 N 5 1.80 0 2 16 Male 33 3 N 11.3 N 3 2.00 0 1 17 Male 59 3 Y 3.9 N 5 1.60 0 2 18 Male 56 3 Y –0.9 N 4 2.00 0 3 19 Male 67 7 Y 17.3 N 4 2.50 1 2 20 Male 58 3 N 19.6 N 5 1.80 5 1 21 Male 47 1 Y 17.9 N 4 1.75 0 2 22 Male 55 18 Y 19.9 N 4 2.25 0 2 23 Female 78 3 Y –2.9 Y 5 1.60 2 0 24 Male 63 3 N 18.4 N 4 1.75 0 3 25 Female 56 1 N –0.4 Y 5 2.00 2 2 26 Male 54 3 Y –3.3 Y 3 2.00 3 2 27 Male 48 1 Y 1.6 Y 4 2.50 3 4 28 Male 60 5 N 1.6 Y 5 1.60 2 1 29 Male 57 3 N 7.7 Y 5 1.80 0 4 30 Male 61 3 N –2.8 Y 4 1.75 0 1 N, case number; Sx, symptom; Y, yes; N, no; SKD, segmental kyphotic deformity; MCS, mean compression score; OPLL, ossification of posterior lon- gitudinal ligament.
