We used bootstrapping to estimate variation in the microsimulation results, accounting for parameter uncertainty and Monte Carlo variation. Bootstrapping repeated the microsimulation for each population 1000 times; additional iterations did not change results at the reported level of precision. For each repetition we randomly selected each parameter from its 95% confidence interval (CI). We report the means of the repetition results. We created the software used to estimate the Markov models and conduct the microsimulations using SAS IML (Cary, North Carolina). The Institutional Review Board (IRB) at [blinded for review] determined that this research did not require IRB review.
Sas iml
Manufactured by SAS Institute
Sourced in United States
SAS/IML is a component of the SAS software suite that provides a matrix programming language for data analysis, statistical modeling, and numerical computation. It allows users to perform advanced statistical and mathematical operations on data, including matrix manipulation, simulation, and optimization. SAS/IML is designed for researchers, statisticians, and data analysts who require a flexible and powerful tool for their data analysis needs.
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Lab products found in correlation
4 protocols using sas iml
1
Bootstrapping for Microsimulation Uncertainty
2
Survivin Immunoassay for Prognosis
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Statistical analyses were performed with SAS/STAT and SAS/IML software version 9.4 (SAS Institute Inc., Cary, NC, USA) or Prism 8. Plots were generated with Prism 8. Median and inter-quartile ranges were used to describe the distribution of continuous variables. Groups were compared using the non-parametric Wilcoxon signed-rank test. The performance of survivin spPLA was evaluated by receiver operating characteristic (ROC) analysis. Logistic regression was performed to assess the risk of survivin concentrations being above the cutoff.
3
Evaluating TYLCV Suppression Strategies
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A Generalized Linear Model (GLM) was fit for the binary outcome TYLCV and classification variables insecticide treatment and waiting period before inoculation with whiteflies from the TYLCV colony (days after treatment [DAT]). Tukey–Kramer multiple pairwise comparisons were carried out and the corresponding letter groupings assigned in order to group the proportions in homogeneous groups of significantly greater proportions. The statistical analysis was performed in SAS/GLM using SAS/STAT and SAS/IML software, Version 9.3 of the SAS System for Windows (
SAS 2011
).
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A Generalized Linear Model (GLM) was fit for the binary outcome TYLCV and classification variables insecticide treatment and waiting period before inoculation with whiteflies from the TYLCV colony (days after treatment [DAT]). Tukey–Kramer multiple pairwise comparisons were carried out and the corresponding letter groupings assigned in order to group the proportions in homogeneous groups of significantly greater proportions. The statistical analysis was performed in SAS/GLM using SAS/STAT and SAS/IML software, Version 9.3 of the SAS System for Windows (
SAS 2011
).
4
Cohort Trends in Binge Drinking
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