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Sas v 9.4 for windows

Manufactured by SAS Institute
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SAS V.9.4 for Windows is a comprehensive software package designed for statistical analysis, data management, and reporting. It provides a robust and versatile platform for data processing, modeling, and visualization. The core function of SAS V.9.4 is to enable users to effectively analyze and manage their data, without making any claims about its intended use or potential applications.

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27 protocols using sas v 9.4 for windows

1

Dendrometers as Water Proxies in Plants

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In the absence of additional NMR sensors to measure water content directly, dendrometers were used as a proxy (De Swaef et al., 2015 (link) and references therein). LVDTs (root and aquatic plant dendrometer DRO, Ecomatik, Dachau/Munich, Germany; sensor range 11 mm, resolution 2.6 μm) were placed on the hypocotyl at mid-height (Figure 1C).
Leaf thickness variation was measured with a miniature displacement transducer (DF-5.0, Solartron Metrology, Leicester, England; sensor range 5 mm, resolution rated “infinite,” limited only by the controlling hardware). The sensor was placed on the adaxial surface of one young- or middle-aged leaf; the dendrometer was held by a custom-built support (Figure 1D). To minimize the pressure on the leaf surface, the sensor rod was not pressed onto the leaf by means of a spring, but was simply placed on the leaf surface and held there by gravity alone.
The dendrometer data were logged and stored every 5 min (hypocotyl: HOBO U12 4-Channel External Data Logger – U12-006, Onset, MA, USA; leaf: CR1000, Campbell Scientific, Logan, UH, USA). The dendrometer data were analyzed using SAS v9.4 for Windows (Statistical Analysis System, SAS Institute, Cary, NC, USA). Hourly measurements were extracted as described by Deslauriers et al. (2011) (link). A smoothing degree of 2 on a scale from 0 to 10 was used to avoid the loss of significant daily variation.
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2

Analyzing Functional Outcomes in Medication Users

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Descriptive statistics were used to report the baseline characteristics of the study population. The one-way analysis of variance was used to compare continuous variables including age, body mass index, functional comorbidity index, MMSE and CES-D, while the χ2 test was used to compare categorical variables including sex, smoking, presence of cognitive impairment and presence of depression. The prevalence of use of medicines was grouped by Anatomical Therapeutic Chemical class.22
Unadjusted analysis of variance and adjusted analysis of covariance were performed to compare differences in physical or cognitive function between users and non-users. Given that hand grip strength has been shown to be significantly different between men and women with distinct sex-specific cut-off points,23 (link) this performance measure was analysed separately based on sex. All other analyses were not stratified by sex. Ordinal logistic regression was used to compare differences in appetite between users and non-users. Binary logistic regression was used to compare differences in frailty between users and non-users. Analysis was performed using SAS V.9.4 for Windows (SAS Institute). A priori, a p value <0.05 was considered statistically significant.
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3

Inflammatory Mediators and Trauma Outcomes

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Tests for normality were used and continuous variables were analyzed with either the χ2 or Kruskal-Wallis test. Correlation analysis was utilized to determine the relationship between the admission R-time and MA and the specified inflammatory mediators. Results were expressed as correlation coefficients with P values less than 0.05 demonstrating significance. Multivariable linear growth models, controlling for age and ISS, were used to compare the MAR ratio to significant inflammatory mediators. SAS v. 9.4 for Windows (SAS Institute, Cary, NC, USA) was utilized for these statistical analyses. A pairwise, retrospective 1:1 propensity matched sub-analysis was performed using IBM SPSS Statistics® case-control matching algorithm.
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4

Baseline Characteristics Comparison

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We summarized child, family, and financial characteristics using descriptive statistics including frequencies, proportions, medians, interquartile ranges (IQRs), and ranges. To compare baseline characteristics between T3 participants and those either ineligible for T3 or who declined actively or passively to participate, we used Fisher’s exact test and Wilcoxon rank sum test to test categorical and continuous variables, respectively. Two-sided P values <0.05 were considered significant. Analyses were conducted using SAS v. 9.4 for Windows (SAS Institute, Cary, NC).
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5

Predictors of Health-Protective Behaviors

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Analyses were conducted using SAS v9.4 for Windows. Our main dependent measure, the sum of 20 Health-Protective Behaviors (HPB), was non-normally distributed, so we created quartiles (HPB-Q) which provide better prediction than dichotomization (Susser et al., 2006, p. 79 ). We conducted logistic regression by variable (class and continuous) to predict HPB-Q scores, yielding an Odds Ratio, 95 %-CI, and raw R-square. A positive OR indicates association with a higher quartile score, i.e., greater HPB use than the lowest-use quartile (base). Whenever the independent variable is a continuous scale score, the Odds Ratio is interpretable as the increase for 1 scale-point. We then re-ran each model entering the 7 demographic variables hierarchically to yield an adjusted-OR (aOR). Significant findings refer to the aORs, except as noted. Some variables had reduced n’s when not applied to all participants, e.g., work-related questions. Given the large sample and the exploratory aim of the study, we kept alpha at 0.05. Finally, we completed stepwise multiple logistic regressions to determine those variables adding unique variance in HPB-Q.
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6

Twin Developmental Outcomes Risk Factors

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For each risk variable, the ‘least risk’ category (eg, not early preterm birth) was used as the reference category (table 1). To estimate the risk of a child being classified as DV1 and DV2, a generalised linear mixed model with a logit link function was used with a random intercept for each twin pair. A total of 30 maternal, pregnancy, birth, child and sociodemographic risk variables were considered for the multivariable models. For DV1, DV2 and each of the five AEDC domains, 24 risk variables were included in the multivariable models; six risk variables were excluded from multivariable analysis due to the prevalence being too small (total n<50 for a given category of a given variable). The variables excluded were: (1) placenta praevia, (2) placental abruption, (3) cephalopelvic disproportion, (4) prolapsed cord, (5) precipitate delivery and (6) a 5-min Apgar score of <7. All variables were added simultaneously to the models. OR and the associated 95% CIs were estimated for both unadjusted and adjusted models. All analyses were undertaken using PROC GLIMMIX in SAS V.9.4 for Windows.55
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7

Bioequivalence Analysis Using SAS

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Statistical analysis was conducted using SAS v9.4 for Windows. PK equivalence was established if 90% confidence intervals (CIs) for the ratios of the geometric means for Cmax, AUC0–t, and AUC0–∞ were within the range 80.0–125.0%.
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8

Pharmacokinetic Analysis of Novel Compound

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Statistical analysis was conducted by SAS v9.4 for Windows. The safety analysis was mainly summarized by descriptive statistics. The number of subjects, onset time, and incidence of positive ADA and the corresponding titer of ADA, were described by treatment groups at scheduled sampling time points. Descriptive statistics of PK parameters were summarized by dose groups. An exponential power model and/or dose normalization method was used to primarily evaluate the main pharmacokinetic parameter (AUC and Cmax).
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9

Epidemiology of Cardiometabolic Conditions

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Statistical analyses were conducted using SAS V.9.4 for Windows (SAS Institute, Cary, North Carolina, USA). The descriptive analysis was done to characterise the study population. Categorical variables are summarised as proportions whereas continuous variables are summarised as medians (Q1–Q3). We used the Shapiro-Wilk test to examine the distribution of data. We used the modified Poisson model to estimate the adjusted prevalence ratios for the following outcomes: current smoking, high LDL, high HbA1c, high systolic blood pressure, low PA and high BMI. A separate model was fitted for each outcome. Each model included the following six predictors: age, sex, place of domicile, SES, insurance status and type of CAD treatment taken. The prevalence ratios are presented with their 95% CIs. The regression coefficients were tested using the Wald statistic. We used the Bonferroni correction to account for multiple comparisons in the subgroup analysis and a threshold of 0.008 was used for testing significance of associations.
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10

Evaluating Modified Naranjo Scale Agreement

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In order to quantify the level of agreement in the modified Naranjo scale, intraclass correlation coefficients (ICCs) with a 95% CI were calculated using the methods described by Shrout and Fleiss.20 (link) ICCs were interpreted according to the following criteria:<0.40, poor agreement; 0.40–0.75, moderate agreement and >0.75, excellent agreement.21
Inter-rater (multirater) reliability for the modified Naranjo scale and the modified FDA algorithm was analysed using Fleiss’ κ with a SE.22 (link) Fleiss’ κ values were also calculated for each question of the modified Naranjo scale. The 95% CI of Fleiss’ κ was calculated from its SE. Fleiss’ κ values were interpreted according to the criteria defined by Landis and Koch23 (link): −1.00, total disagreement; 0.00, no agreement; 0.01–0.20, slight agreement; 0.21–0.40, fair agreement; 0.41–0.60 moderate agreement; 0.61–0.80 substantial agreement; 0.81–0.99 almost perfect agreement and 1.00, perfect agreement. All statistical analyses were performed using SAS V.9.4 for Windows (SAS Institute Inc, Cary, North Carolina, USA).
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