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153 protocols using sas enterprise guide

1

Secure Healthdata Platform for Research

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The Healthdata.be platform not only enables safe data transfer but also provides a secure environment for data handling and data analysis for research purposes. Coded data are received by the HD4RES (Healthdata for research) service, which shows the data as sent by the data provider. The interface of the HD4RES is almost identical to that of the HD4DP, except that identification details are coded. Upon arrival in the HD4RES, the data are not yet stored in the datawarehouse (DWH) of Healthdata.be. The DWH has 3 separate entities—the validation environment, the analysis environment, and the reporting environment—and uses SAS Enterprise Guide (SAS Institute Inc) to visualize and process the data. It is first stored in a validation table where data quality is controlled. Healthdata.be allows for semiautomated processes so that the validation of continuous data capture can be operationalized. Once validated, data are then promoted to the analysis environment of Healthdata.be. Access to the HD4RES and the separate environments of the DWH are secured through a 2-factor authentication and can be restricted depending on the needs of the researcher. Furthermore, data processing and reporting can be operationalized to accommodate a continuous data flow in ongoing registers.
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2

Trends in Antiseizure Medication Prescriptions

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Overall trends of individual ASM prescriptions and prescription patterns according to age and sex were described for the period from 2009 to 2017. Patients were classified into the following three age groups to evaluate the effects of age on the selection of prescription patterns and types of ASMs: 1) <20 years, 2) 20–59 years, and 3) ≥60 years. Each ASM duotherapy group was defined according to two individual ASMs contained; for example, a combination of VPA and CBZ would simultaneously classified as VPA combinations and CBZ combinations.
Changes in annual trends of ASM prescriptions in each age and sex group were assessed using a Poisson regression model. Values were expressed as average annual percentage changes (AAPCs), which quantify the estimated yearly percentage changes in prescription rates over a specified time interval.
Calculations were performed by exponentiating the coefficient of regression to obtain AAPC values. Data were analyzed using SPSS (version 26.0, IBM Corp., Armonk, NY, USA) and SAS Enterprise Guide (version 7.1 for Windows; SAS Institute, Cary, NC, USA). A p value<0.05 was considered statistically significant.
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3

Ultrasound Scoring and Hepatic Assessment

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Data were analyzed using GraphPad Prism (Version 9.0; San Diego, CA, USA) and SAS Enterprise Guide (Version 7.13; Cary, NC, USA).
Subject characteristics were compared using the Mann Whitney U test. The Friedman test compared differences between ultrasound assessment using SP, HPP1, and HPP2 in the current paired design with post test results allowing for interpretation of directionality. Within-probe performance was tested via the Kruskal-Wallis test with post testing to interpret effect sizes. Post tests were adjusted for multiple comparisons. Linear regression tested dependency of measures of body size relative to ultrasound scoring. General alpha-level was set at 0.05.
One-way-ANOVA was used to test for differences in CAP between BMI groups. The cutoff for optimal visualization of the liver and right kidney as performed using the total ultrasound score was set at 32 points via calculating the mean score achieved within BMI group I, in a setting of poorer imaging quality for BMI groups II through IV across probes (unpaired t test: P < 0.0001; − 7.82 points, 95% confidence interval − 7.82±1.40 points).
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4

TKI Therapy and Clostridium Difficile Risk

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The baseline characteristics of the study patients are presented as numbers and percentages, means and standard deviations (SDs), or medians and interquartile ranges (IQRs), if appropriate. We used multivariate Cox proportional hazards regression models to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of CDI. Afatinib users were considered the reference group because afatinib is a newer generation TKI associated with more diarrhea events.22 (link) We considered results with a two-sided P value < 0.05 to be statistically significant. Statistical analyses were performed using the SAS Enterprise Guide (Version 7.1; SAS Institute Inc., Cary, North Carolina).
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5

Statistical Analysis of Experimental Treatments

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Statistical analyses were conducted using the SAS Enterprise Guide (ver. 4.3, SAS Institute Inc., Cary, NC, USA). Then, significant differences among the treatments in these experiments were evaluated using Tukey’s studentized range test at p < 0.05.
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6

Sindbis Virus Seroprevalence Study

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Frequencies were calculated for the demographic characteristics distribution of the study population. Seropositivity rate was calculated by the number of positive samples divided by the number of samples tested. Logistic regression analyses were applied to assess the factors associated with seropositivity to Sindbis virus. The level of significance was determined at p-value of 0.05. Statistical analyses were performed using the SAS Enterprise Guide software package (version 7.12, SAS Institute Inc., Cary, NC, USA).
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7

Post-Discharge Healthcare Utilization Analysis

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For both samples, means and standard deviations for age; median and ranges for SES, total 2015 spending, and number of morbidities; frequencies and percentages for sex, marital status, types of morbidities, and the three most frequently registered DRGs opened at admission, were calculated. We analyzed the six-month post-discharge healthcare utilization for all FE and FE admitted due to a hip fracture, for hospital care, GP care, long-term care, and community nursing care. We did this by calculating the number and percentage of chronically ill older adult patients with a certain type of treatment and by calculating the average number of treatments per user. To assess group differences, a T-test and a non-parametric equivalent were performed on all continuous variables. A Chi-square test was performed on all categorical variables.
All analyses were performed in SAS Enterprise Guide.
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8

Inappropriate and Excessive SABA Use

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SAS Enterprise Guide (Version 6.1, Cary, NC, USA) was used for all analyses. The unit of observation was each patient-year of follow up. Generalised linear models with generalised estimating equations (with a binomial distribution and logit link function, given the binary outcomes) were used to account for the clustered nature of the data (multiple observation units within the same patient). The binary dependent variable indicated inappropriate (primary outcome) or excessive (secondary outcome) SABA use. The aforementioned covariates entered the model as independent variables. All afore-mentioned variables were simultaneously included in the regression model and adjusted for. We excluded the periods in which individuals had no record of any asthma-related resource use (hospitalisation, outpatient visits, or medication dispensation) from the regression analysis, as these periods likely represent dormant asthma. However, in a sensitivity analysis we included such periods and repeated all analyses.
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9

Statistical Analysis of Avian Eimeria Detection

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Statistical analysis of data was performed using SAS Enterprise Guide (Version 7.15, SAS Institute Inc., Cary, NC, USA) and graphs were created with GraphPad Prism (Version 9.2, GraphPad Software, LLC, San Diego, CA, USA). Descriptive statistics were used to analyze clinical signs and gross lesions. For evaluation of significant differences in BW between groups and between EC-positive and EC-negative birds in the EC14 groups, we performed the Kruskal-Wallis test and Mann-Whitney U test, as conditions of normality and heterogeneity of variance were not met. Isolation rates of culture and EC-DNA detection in real-time PCR were compared between groups using the Fisher’s Exact Test. The Bonferroni-Holm correction method was used to adjust p-values for multiple testing where applicable [32 ]. Results from bacteriological examination and real-time PCR of the spleen within the same group were compared using the Kappa coefficient and McNemar’s Test. The Kappa coefficient was interpreted as published by Landis and Koch in 1977 [33 (link)]. Differences were considered significant at p ≤ 0.05.
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10

Oxygen Needs in COVID-19 Pneumonia

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For primary analysis, we calculated summary descriptive statistics, median and interquartile range, for patients discharged while receiving home oxygen for COVID-19 pneumonia. Data were collected in MS Excel 2016 (Microsoft). Statistical testing was performed using SAS Enterprise Guide, version 7.1 (SAS Institute Inc).
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