Spatial analysis of yield monitor data was conducted using ArcMap vr. 10.3.1 (ESRI). In 2014, a paired t-test was used to compare the effect of irrigation scheduling methods on grain yield within each soil-textural zone (PROC TTEST) implemented in SAS 9.1 (SAS for Windows v. 9.1, SAS Institute Inc., Cary, NC). As indicated by the two tales significance probability (P values < 0.0001) there is evidence that the variances for the two irrigation levels are unequal. Thus, the Satterthwaite method was used. In 2015, a split plot completely randomized design was implemented with soil-textural zones as main plot and irrigation scheduling methods as subplots and grain yield differences due to irrigation scheduling within and between zones were tested using generalized linear mixed models (PROC GLIMMIX) implemented in SAS 9.1 (SAS for Windows v. 9.1, SAS Institute Inc., Cary, NC).
Sas for windows v9
Manufactured by SAS Institute
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SAS for Windows v9.4 is a software application designed for data analysis and statistical modeling. It provides a comprehensive suite of tools for data management, exploration, and visualization. SAS for Windows v9.4 is a powerful platform for advanced analytics, enabling users to extract insights from complex data sets.
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Lab products found in correlation
65 protocols using sas for windows v9
1
Spatial Analysis of Yield Monitor Data
2
Efficacy Analysis of Eribulin in Cancer Studies
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The statistical methods have been published with the primary analyses for Studies 206 and 208 (McIntyre et al. 2014 (link); Wilks et al. 2014 (link)). Efficacy analyses were based primarily on the full analysis set (FAS), which included all patients who received ≥1 dose(s) of study treatment. The safety analysis set included all patients who received ≥1 dose(s) of eribulin and had ≥1 postbaseline safety evaluation. All efficacy endpoints were summarized descriptively. Kaplan–Meier method was used to estimate the time to event variables (e.g., PFS). Greenwood method was used to construct 95 % confidence interval (95 % CI) for the median. Exact method was used to construct 95 % CI for rate variables (e.g., ORR). Statistical analyses and summaries were performed using SAS for Windows v. 9.3.
3
Evaluating Continuous and Categorical Data
4
Feasibility Study of MACE Outcome
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All statistical analyses were specified in the protocol and described in a detailed statistical analysis plan prior to the final unblinded analysis, which was undertaken by the trial statisticians (AM and CB). We calculated the OR for achie ving the primary efficacy outcome by logistic regression with the primary outcome as the dependent variable. An initial binary logistic regression analysis was undertaken to yield a simple unadjusted OR with a 95% CI. For the primary analysis (from which conclusions are drawn), important covariates were also entered into the model (age, gender, trial centre, MACS risk group, cardiovascular risk factors and a prior history of coronary artery disease). Similarly, the absolute rate of MACE (secondary outcome) at 30 days, 3 months and 6 months was compared between groups using logistic regression. Length of stay and patient satisfaction data were compared between randomised groups using the Mann-Whitney U test. Finally, we calculated the incidence of MACE at 30 days, 90 days and 180 days stratified by MACS rule risk group in all participants. Statistical analyses were undertaken using SAS for Windows V.9.3. As this is a feasibility study, no formal sample size calculation was undertaken but we aimed to evaluate recruitment rate over a fixed period.
5
Hippocampal PET Imaging After Anesthesia
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Statistical analysis system (SAS) for Windows (v9.3) was used in the data analysis. SigmaPlot for windows (v11.0) was used in generating graphs. The averaged value of bilateral hippocampal region SUVs collected from each frame in the scan was used in the statistical analysis. The average SUVs of groups are presented as mean ± SEM. Since the data in each subject were acquired by repeated measures, the linear mixed effect model was utilized in the analysis. The effects of treatments (air versus sevoflurane exposure), 7-NI co-administration, and time interval between the exposure and microPET scan (1 week versus 2 or 4 weeks) were examined in the analysis. Dunnett’s multiplicity adjustment test was used for comparisons with the control in order to preserve the overall type I error rate at the nominal 5% level. Statistical significance was considered when the p-value is <0.05.
6
Cocaine Addiction and Stress Phenotypes
7
Islet Damage Analysis Protocols
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For normally distributed data, the analyses were performed using two-way ANOVA followed by the nonparametric test. For scoring islet damage, the linear model for a binomial distribution with a logistic link was used, followed by the multiple Wald comparison test for islet size and architecture. Statistical tests were performed using SAS for Windows, v. 9.3. Statistical significance was considered when P < 0.05.
8
Seasonal Trends in Surgical Cancellations
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The data were typed in an Excel spreadsheet and analyzed using Statistical Analysis Software, SAS for Windows, V.9.3. Descriptive analysis was used, with percentage frequencies for the demographic variables and indicators of surgical cancellation. For the analysis of seasonal trends regarding the number of surgical cancellations, a Poisson regression model adjusted to identify the monthly differences was used.
9
Evaluating Adherence and Cost-Effectiveness
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All analyses were performed on an intention to treat basis using SAS for windows v9.3.
Categorical variables are summarised as number and percentage (n(%)). Continuous variables were summarised by mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate. Adherence data were considered as those who recorded no exercise sessions per four week period, non-adherence (<75% of completed sessions) and adherence (≥75% of completed sessions) and was compared between intervention groups using Chisquared tests. Between group differences were assessed using analysis of covariance (ANCOVA) adjusted for baseline value and stratification variables (centre and EDSS) and
Cohen's (d) effect sizes were calculated (26) .
Cost-effectiveness was explored using healthcare resource use and valued using UK cost sources (27) (28) (29) . EQ-5D data were used to derive health utility values and estimate quality-adjusted life-years (QALYs) gained (30) . Mean costs and QALYs associated with each treatment group were estimated using generalised linear models. Telephone interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. One researcher first coded all scripts, then two researchers independently identified emerging themes and sub-themes. Following this, discussion was held between the researchers to agree and finalise themes and sub-themes.
Categorical variables are summarised as number and percentage (n(%)). Continuous variables were summarised by mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate. Adherence data were considered as those who recorded no exercise sessions per four week period, non-adherence (<75% of completed sessions) and adherence (≥75% of completed sessions) and was compared between intervention groups using Chisquared tests. Between group differences were assessed using analysis of covariance (ANCOVA) adjusted for baseline value and stratification variables (centre and EDSS) and
Cohen's (d) effect sizes were calculated (26) .
Cost-effectiveness was explored using healthcare resource use and valued using UK cost sources (27) (28) (29) . EQ-5D data were used to derive health utility values and estimate quality-adjusted life-years (QALYs) gained (30) . Mean costs and QALYs associated with each treatment group were estimated using generalised linear models. Telephone interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. One researcher first coded all scripts, then two researchers independently identified emerging themes and sub-themes. Following this, discussion was held between the researchers to agree and finalise themes and sub-themes.
10
Smoking Prevalence and Occupational Factors
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The age-standardised prevalence rates of current smoking and heavy smoking, stress, depression, suicidal thoughts, working hours and education about smoking cessation were calculated from data for each wave of KNHANES. Direct standardisation to 10 year age groups was performed using the age distribution of the 2005 and 2010 South Korean census populations as the standard population. Logistic regression analyses were performed to analyse the relationship between current or heavy smoking rate and occupational group in terms of the prevalence ratio (PR). Trends in the OR and PR were estimated by examining the p value for an interaction term of occupations and the variables that identified the year of the data in the model. SAS for Windows V.9.2 (SAS Institute, Inc., Cary, NC, USA) was used for statistical analyses.
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