Stata mp 16

The clinical characteristics of the patients were summarized, and specific subgroups were described using descriptive statistics. After descriptive analyses, the categorical variables of the groups were compared using the Fisher’s exact test or Pearson’s chi-square test, while their continuous variables were compared using the Student’s t-test and ANOVA test. In this study, the initial glucose level (≥ 140 or < 140 mg/dl) and NIHSS (≥ 10 or < 10 points) were considered as categorical parameters.
Univariate analyses were performed to identify the odds ratio across the outcome parameters (END, in-hospital death, poor functional outcome at 90 days and 1-year). Also, logistic analyses were performed with clinical variables and laboratory results. Age, sex, baseline NIHSS, stroke subtype, atrial fibrillation, previous stroke, smoking, LDL cholesterol level, onset to needle time, mechanical thrombectomy, and baseline blood glucose level for END, in-hospital death, poor outcome at 90 days or poor outcome at 1-year were estimated by a logistic regression analysis [11 (link), 18 (link)].
The odds ratios (ORs) and 95% confidence interval (CI) were calculated. All P-values were 2-sided, and statistical significance was defined as a P-value less than 0.05. STATA/MP 16.1 (StataCorp., College Station, TX, USA) was used in all the statistical analyses.

All statistical analyses were performed using Stata/MP 16.1. Statistical tests of normality were performed using the Shapiro–Wilk test. Demographics were compared between groups using either linear regression (age and education) or a chi-squared test (sex). Linear regressions adjusting for age and sex were used to analyse the MMSE, CDR® plus NACC FTLD and PASS scores. Both linguistic and non-linguistic symptoms were compared in each disease group versus controls using linear regressions adjusting for age and sex and 95% bias-corrected bootstrapped confidence intervals with 2000 repetitions (as there was minimal variation from 0 in severity scores for the control group). Comparison of these symptoms between groups used an ordinal logistic regression adjusting for age and sex. The neuropsychological assessments and cortical and subcortical volumes were compared using linear regression models adjusting for age and sex, as well as scanner type for the imaging analysis; 95% bias-corrected bootstrapped confidence intervals with 2000 repetitions were used if data were not normally distributed.

We summarized our findings by aggregating by age-group-year across all countries within an income group (LICs, LMICs, and UMICs). To do so, we estimated the age-group-weighted mean VSMU for each age-group-year. To compute the age weights, we used country-specific projections available from the United Nations Population Division’s World Population Prospects [9 ]. However, since age-group-specific population projections were only available for 2020, 2025, and 2030, we aggregated results and estimated 95% uncertainty ranges (URs) for these 3 years only.
All computational simulations were conducted using Stata MP/16.1 [24 ].

First, we used descriptive statistics to characterize the analytic sample in terms of demographics, cigar use behaviors and preferences, and use of other substances and tobacco products. Next, we calculated the mean importance of each cigarillo product characteristic overall, as well as by demographics, cigar use behaviors and preferences, and use of other substances and tobacco products. We then examined differences in mean importance using t-tests for variables with two categories and one-way ANOVA tests for variables with more than two categories. For significant ANOVA tests, we ran post-hoc pairwise comparisons using Bonferroni adjustment to identify which categories differed from one another in terms of product importance. All statistical tests were conducted in Stata/MP 16.1 [22 ].

Proportions and means were generated to describe CPI coverage in relation to total intake (g), energy (kcal), macronutrients (g), total sugar (g), sodium (mg), total dietary fibre (g) and food groups (g). Quartiles of CPI coverage were then assigned based on the proportion of total individual intake (g) captured by the CPI. Group mean intakes of energy and food components were compared across quartiles, applying a Bonferroni correction to obtain a Bonferroni corrected P-value (α = 0·05).
The analysis was conducted on STATA/MP 16.1 statistical software. The CCHS-N Public Use Microdata File was accessed through the Data Liberation Initiative at Dalhousie University. Accompanying Statistics Canada sampling weights were used to create weighted point estimates which represented the 34·5 million Canadians based on the Canadian Dietary Reference Intake age-sex groups and the 2011 Canadian Census population size^{(18 )}. The Canadian Dietary Reference Intake age-sex groups refer to Health Canada groupings used to determine the recommended intakes of nutrients and energy for a healthy individual based on their age and sex^{(23 )}. Bootstrap replication weights (B 500) provided by Statistics Canada were used to generate 95 % CI that took into account the multi-stage survey design used by the CCHS-N^{(18 )}.