The differences in patient and procedure characteristics according to spine surgeon specialty were tested using the χ2, Fisher exact, or Student’s t-test, as appropriate. We used multivariate logistic regression to evaluate risk factors or early predictors and maintained frequencies greater than 10 and also had P values < 0.2 in the initial univariate testing [21 (link)]. Variables that were missing in more than 20% of the cohort were excluded to avoid model distortion [22 (link)]. Odds ratios (OR) and 95% confidence intervals (CI) were reported for both the univariate and multivariate analyses. The variables with P values < 0.05 with OR and 95% CI exclusive of 1.0 in the multivariate test were regarded as significant independent predictor [23 (link)]. We measured the discriminative power and the goodness of fit of the predictive model using the C-statistic and the Hosmer and Lemeshow test, respectively. All analyses were performed using Stata version 14.0 MP (StataCorp LP, College Station, TX).
Stata version 14.0 mp
Manufactured by StataCorp
Stata version 14.0 MP is a software package designed for statistical analysis, data management, and graphics. It provides a comprehensive set of tools for researchers, analysts, and professionals working with data. The 'MP' in the name stands for 'Multiple Processors,' indicating that the software is optimized for parallel processing, allowing for faster computation on systems with multiple cores or processors.
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Lab products found in correlation
2 protocols using stata version 14.0 mp
1
Spine Surgeon Specialty Risk Factors
2
Physical Activity's Impact on Healthcare Expenditure
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Data was obtained in 2017. We first performed univariate analyses to describe the distributions of annual health care expenditure, demographic characteristics, socioeconomic status, and health conditions by physical activity adherence. We used a Pearson χ2 test to examine whether expenditure and other covariates varied significantly between groups. We further employed multivariable generalized linear models with a log link and gamma distribution to model the relationship between annual expenditure and physical activity. Lastly, we conducted stratified analyses to explore whether the impact of physical activity on expenditure was consistent across the extent of survival. Since MEPS adopts a multi-stage design with weights to reflect nonresponse rate, we conducted all the analyses adjusted for this complex survey design and weight using svyset command (Machlin et al., 2005 ) in Stata version 14.0 MP (StataCorp, College Station, TX) to produce national estimates. A p-value <0.05 was considered statistically significant.
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