Data Analyses. Preliminary analyses Pre- and posttreatment - controlled data Follow-up measurements – uncontrolled data
Data Analyses. All analyses were stratified by the severity of hemophilia, and often by age category as well. As the clinical characteristics of hemophilia A and hemophilia B do not differ, we present combined results for hemophilia A and B. Data on the treatment modality, the number of bleeding episodes, the use of hospital facilities, and absence from school or work referred to the year that preceded the questionnaire surveys (2000). Children were defined as patients younger than 16, adolescents as patients between 16 and 25 and adults as patients older than 25 years. The use of prophylaxis refers to patients who received prophylaxis as their main treatment modality, excluding patients who received a combination of on demand treatment and prophylaxis during risk periods. Absence from school was calculated only for that part of the population that followed a full-time education. Absence from work was calculated for patients aged 16 to 65 who had a paid job (full-time or part-time). The inactivity ratio was calculated as the ratio of inactivity in the study population and inactivity in Dutch men. Patients that did not have a full-time or part-time paid job were defined as inactive. Descriptive statistics for age, the use of hospital facilities, absence from work and employment were compared to national figures for the general male population that were provided by the Central Bureau of Statistics Netherlands Statline database20. Self reported measures on joint impairment were obtained for a series of 16 joints which are, the neck, the left and right shoulder, the back, the left and right elbow, the left and right wrist, the left and right hand and fingers, the left and right hip, the left and right knee and the left and right ankle. The possible scores were 0 (no impairment), 1 (some impairment without daily problems), 2 (some impairment with daily problems), and a maximum of 3 (severe impairment with complete loss of function). From scores of the 16 separate joints a joint score was calculated with a minimum score of 0 and a maximum score of 48 points. As joint impairment was reported most frequently in the ankles, elbow and knees these were analyzed separately. Response was 70% in 2001, compared to 84% in 197219, 70% in 197821, 81% in 198522 and 78% in 199218. One hundred and ninety eight patients participated in all 5 surveys. Table 1 shows the characteristics of participants in each of the 5 surveys. The mean age of participants increased from 21 years (median 19; ran...
Data Analyses. To compare the incidence of plural agreement over dialects, pronoun types, and head-noun types, analyses of variance were performed on the proportions of validPlural responses for each participant andeach item in each cell of the experimental design. The proportions were calculated relative to all valid Plural and Singular re- sponses in each condition for each type of preamble. Prior to analysis the proportions were arcsin transformed(Xxxxx 1976). Analyses were performedwith both participants and items as random factors using the min F′ statistic (Xxxxx 1973). Unless otherwise indicated, effects reported as significant were associated with probabilities less than or equal to 0.05, and the corresponding test statistics are summarized in Appendix D. Type of preamble presentation (read-aloud or reproduction) was treated as a separate factor in the analyses. Because the major findings were similar regardless of presentation mode, we omit differences associated with presentation from the results and discussion.
Data Analyses. Respondent shall analyze and evaluate both preexisting and newly collected data to describe:
(1) Site physical and biological characteristics; (2) contaminant source characteristics; (3) nature and extent of contamination; and (4) contaminant fate and transport. Results of the Site physical characteristics, source characteristics, and extent of contamination analyses are utilized in the analysis of contaminant fate and transport. The evaluation must include the actual and potential future magnitude of releases from the sources, and horizontal and vertical spread of contamination as well as mobility and persistence of contaminants. Where modeling is appropriate, such models shall be identified to EPA in a technical memorandum prior to their use. All data and programming, including any proprietary programs, shall be made available to EPA together with a sensitivity analysis. The RI data shall be presented in an electronic format). The validated data, along with QA/QC information and data validation summaries, shall be submitted in electronic format within 30 calendar days from receipt of data for the last sample of each sampling event from the laboratory. Respondent shall then collect any data required to address data gaps identified by EPA as needed. This evaluation shall also provide information relevant to Site characteristics necessary to evaluate the need for remedial action and aid in the development and evaluation of remedial alternatives. Analyses of data collected for Site characterization must meet the DQOs developed in the QA/QC plan stated in the SAP (or as revised during the RI).
Data Analyses. Data were entered into an electronic database and translated into English using the original English survey as a reference. Descriptive statistics of all characteristics and factors of interest by intention to intervene for each outcome of interest are summarized in Table 1. Initially, age and academic year were considered in the analytical models, however, age was removed due to evidence of multicollinearity using the variance decomposition proportion value (VDP1 > 0.7). A two-step multiple imputation method of missing values was performed to allow maximum utilization of available data. This method was recommended for imputing arbitrary patterned continuous variables and models containing mixed covariates (Xxxxx & Kosten, 2017). Multivariate Poisson regressions with robust variance analyses were performed using PROC GENMOD with a log link function to produce prevalence ratio (PRs), 95% confidence intervals (CI), standard errors (SEs), and p-values. Four models were examined with the intention to intervene in each binary outcome. Models 1.a & 1.b represent the regression results for the outcome of interest where friends are perpetrators, Models 2.a & 2.b represent the regression results for the outcome of interest where strangers are perpetrators (Table 2). Models 1.b. & 2.b. adjusted for 4 bystander variables based on the 5-step situational bystander model. Clustering by main faculties ( Humanities, Health, and Sciences) was controlled for in the imputation and regression analyses models. All statistical analysis and imputations were performed using SAS 9.3.
Data Analyses. Scale reliabilities, descriptive statistics, intercorrelations, and multiple linear regression models were computed using SAS software, Version 9.4 (SAS Institute Inc.) or IBM SPSS Statistics, Version 26 (IBM Corp.). Path models were assessed using Structural Equation Modeling (SEM) with Mplus Version 8.6 (Xxxxxx & Xxxxxx, 1998-2017). Multi-item scales were specified in such models as latent constructs, each measured, relying on the accepted approach of parcelling (Little et al., 2013), with three indicators calculated as random thirds of the scale items. Participants’ sociodemographic and medical characteristics were modelled as observed variables. In models involving multiple measurements of the same construct, variance resulting from specific measurement occurrences was accounted for by correlating all the measurement errors of same indicators across time points (Xxxxx & Xxx, 1996). To assure weak factorial invariance, factor loadings were constrained for equality across measurement waves. As there were missing values in the data, and the data deviated from normality, we used the Mplus MLR estimator that allows for maximum likelihood estimation with robust standard errors and chi-square calculation in presence of missing values (Xxxxxx & Xxxxx, 2003). Following recommendations of Xx and Xxxxxxx (1999), we report two fit indexes: Xxxxxx-Xxxxx Index (TLI) and Comparative Fit Index (CFI), and two indexes of misfit: Root Mean-Square Error of Approximation (RMSEA) and Standardised Root Mean-Square Residual (SRMR) are reported. TLI and CFI close to or above 0.95, combined with RMSEA below 0.06 and SRMR below 0.08, are considered indicative of acceptable fit.
Data Analyses. All smallmouth bass captured within each of the sub-reaches will be enumerated in 2020-2021 similar to that during former years (2004 – 2019). Total numbers of smallmouth bass, largemouth bass and walleye collected and catch/effort (fish/hr) will be also determined for each sub-reach per sampling pass. Length data will be recorded for 2020-2021 similar to that during former years (2004 – 2019) to determine the size structure of smallmouth bass removed.
Data Analyses. To evaluate if the observed decline in number of norovirus outbreaks by NoroSTAT can be attributable to reduced exposure due to non-pharmaceutical interventions, or seasonal trends, cyclic cubic generalized additive models (GAMs) are used to compare the number of monthly outbreaks before and after NPI policies are implemented (COVID era vs. non-COVID era), adjusting for seasonal trends, stratified by both state and setting. For all incidence models, the March 2020 was excluded because it was a transition month, during which NPIs were gradually introduced in many states. A regression equation for the overall GAM stratified by state level is shown below: 𝐶𝑜𝑢𝑛𝑡(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘𝑠𝑡) = 𝛽1𝐶𝑂𝑉𝐼𝐷 𝐸𝑟𝑎𝑡 + 𝑠𝑝𝑙𝑖𝑛𝑒(𝑡) Where Count(Outbreakst) is the number of norovirus outbreaks by month, COVID Era is the proxy predictor of non-pharmaceutical interventions, and spline(t) represents the smooth term for month, with a dimension of 12. Results were estimated separately for each of the 9 NoroSTAT states. To explore how the impact of different NPI policies might vary by transmission venue and how relaxation of NPIs might also impact transmission differently (e.g., restaurants, schools/colleges/universities, hospitals, child daycares, long-term care facilities), we ran similar GAM models to the state-specific models for each transmission venue. A regression equation for the overall GAM stratified by setting level is shown below: 𝐶𝑜𝑢𝑛𝑡(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘𝑠𝑡) = 𝛽1𝐶𝑂𝑉𝐼𝐷 𝐸𝑟𝑎𝑡 + 𝑠𝑝𝑙𝑖𝑛𝑒(𝑡) Results are stratified by different settings (restaurants, schools/colleges/universities, hospitals, child daycares, long-term care facilities, and others). To assess if different states distort the association between COVID Era and number of norovirus outbreaks, reported states are controlled in GAM models to evaluate the potential confounding. A regression equation for the GAM model adjusting for states, stratified by setting level: 𝐶𝑜𝑢𝑛𝑡(𝑂𝑢𝑡𝑏𝑟𝑒𝑎𝑘𝑠𝑡) = 𝛽1𝐶𝑂𝑉𝐼𝐷 𝐸𝑟𝑎𝑡 + 𝛽2𝑅𝑒𝑝𝑜𝑟𝑡𝑖𝑛𝑔 𝑆𝑡𝑎𝑡𝑒 + 𝑠𝑝𝑙𝑖𝑛𝑒(𝑡) Where Reporting State represents the 9 states joining NoroSTAT before 2017 where outbreak exposure occurred, and spline(t) represents the smooth term for month with 12 knots. Since different states enacted non-pharmaceutical interventions on different dates and the true effect estimate for setting-level NPIs might differ across states, an indicator variable for state is also included in our analysis to assess the ...
Data Analyses. This section describes the methods used to carry out the analyses on the WISER dataset for uncertainty issues. First is a description of some general data exploration and derivation of macrophyte metrics. This is followed by a description of the methods used to answer the specific research questions.
2.2.1 General data-exploratory work
Data Analyses. The specific water quality parameters to be analyzed at each site in any Operating Year will be determined by the responsible Party or Parties and set forth in the Annual Operating Plan. In an effort to maximize comparability between sites, data will be collected
