We used the survey sample suite of programs implemented in Stata version 12.1 statistical software (StataCorp LP) for all analyses to account for the multistage cluster sampling design of the NHAMCS. We performed bivariable logistic regression to identify the strength of association of race on overall administration of analgesia and on opioid administration specifically. We then performed multivariable logistic regression to adjust for confounding by specified covariates for which there was biological or epidemiological plausibility. Variables with P < .10 in any of the bivariable analyses and any variables that were a priori felt to have a potential relationship with overall analgesia or opioid administration were retained in our multivariable model. Furthermore, we performed tests of interaction between sex and pain score. Estimates derived from the multivariable model included adjusted odds ratios (ORs) with 95% confidence intervals and predicted probabilities. We estimated the predicted probabilities of analgesia and opioid administration by means of predictive margins as implemented in the “margins” command in Stata.25
Stata version 12.1 statistical software
Manufactured by StataCorp
Sourced in United States
Stata version 12.1 is a comprehensive statistical software package. It provides a wide range of tools for data management, analysis, and visualization. Stata version 12.1 includes features for regression analysis, survey data analysis, time-series analysis, and more. The software supports a variety of data formats and can be used across multiple platforms.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using stata version 12.1 statistical software
1
Racial Disparities in Emergency Analgesia
2
Predicting Cardioembolic Stroke Using T_max Ratio
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All statistical analyses were performed using Stata version 12.1 statistical software (StataCorp LP, College Station, Texas, USA). Continuous variables were expressed as means ± SD or median and interquartile range. The differences between the two groups were analyzed with the Mann-Whitney U test or Student t-test after testing for normality. Categorical data were analyzed using Fisher’s exact or Person’s Chi-Square test, as appropriate. In this study, we used threshold-independent receiver-operating characteristic (ROC) curve analysis to estimate the optimal Tmax lag in predicting cardioembolic stroke. The highest values of sensitivity and specificity were used to calculate the optimal diagnostic cut-off point for a (Tmax volume) / (Tmax > 8s volume) ratio predicting cardioembolic stroke. For each ratio of (Tmax volume) / (Tmax > 8s volume) in predicting cardioembolic stroke, a multivariate logistic regression model was constructed to adjust for baseline variables when a p-value <0.1 was found in the univariate analysis. All p values were two-tailed and a p value < 0.05 was considered to be statistically significant.
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