One-way repeated measures analysis of variance tests (RM ANOVA) were run to test whether there was evidence of differences in the means of measures across the timepoints using complete case analysis (i.e., n = 14). If RM ANOVA indicated differences, post-hoc tests were run to explore this further. This was done via estimation of predicted means for each timepoint, and pairwise comparisons of these means to assess between which timepoints differences were being observed and the magnitude of the differences by reporting contrasts. Sensitivity analyses were also conducted with the full sample (different sample sizes across timepoints) to compare with the complete case findings and are presented in the Supplementary material . All analyses were conducted in STATA release 17 (STATA Corp LP, USA).
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Stata Release 17 is a comprehensive statistical software package developed by StataCorp. It provides a wide range of tools for data management, analysis, and visualization. Stata Release 17 includes advanced statistical methods, time-series analysis, and econometric modeling capabilities.
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14 protocols using stata release 17
1
Repeated Measures ANOVA Protocol
2
Margin Analysis in US-Assisted Surgery
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Differences in age and mean deep histopathological resection margin between the US-assisted surgery and control cohorts were analysed by Student's t test and linear regression, and differences in categorical variables were analysed by the Chi-square test or Fisher's exact test when appropriate.
Unadjusted and adjusted Poisson regression with robust standard errors were performed for the two binary definitions of close margins (<5.0 mm and ≤2.2 mm) to compare the two cohorts. Adjustment was made for potential confounders: T stage, free flap, Brandwein (BW) classification and harmonic scalpel. As nine patients had missing BW classifications, the adjusted models were constructed with (n = 101) and without (n = 110) BW classifications. This modified Poisson regression gives relative risks (RR) with 95% confidence intervals (CIs) as association measures. A p value lower than 0.05 was regarded as statistically significant, and analyses were performed in IBM SPSS (Armonk, NY) version 25 and Poisson regression in STATA release 17 (StataCorp, TX).
Unadjusted and adjusted Poisson regression with robust standard errors were performed for the two binary definitions of close margins (<5.0 mm and ≤2.2 mm) to compare the two cohorts. Adjustment was made for potential confounders: T stage, free flap, Brandwein (BW) classification and harmonic scalpel. As nine patients had missing BW classifications, the adjusted models were constructed with (n = 101) and without (n = 110) BW classifications. This modified Poisson regression gives relative risks (RR) with 95% confidence intervals (CIs) as association measures. A p value lower than 0.05 was regarded as statistically significant, and analyses were performed in IBM SPSS (Armonk, NY) version 25 and Poisson regression in STATA release 17 (StataCorp, TX).
3
Analyzing Student Nutrition Program Data
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Survey data were collected directly from REDCap in the Medical Student Elective. In Food Is Power and the Community Cooking & Nutrition classes, they were first collected via a written survey and then imported to REDCap before being described and analyzed. For each arm, paired data were pooled between the 2021–2022 and 2022–2023 collection years. Quantitative analyses were conducted using Stata Release 17 (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX, USA: StataCorp LLC). Given small sample sizes, we calculated exact p-values within these groups. Qualitative data were analyzed using Dedoose.
4
Tertile-Based Comparison of Lung Function
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For comparison of baseline characteristics, subjects were divided into thirds by tertiles based on the % predicted FEV1 score. Comparison of baseline characteristics were made using either analysis of variance (ANOVA) for continuous data or chi‐square test for categorical variables. The data for mean CAL and mean PPD were not normally distributed, so log‐transformed values were used. For descriptive purposes, these variables were summarized as geometric means (interquartile range) rather than as means (standard deviation), which were used to summarize other continuous variables.
Multiple linear regression was conducted in a sequential model design where potential confounding variables were added to produce a fully adjusted final model incorporating established predictors of % predicted FEV1. Model 1 included adjustment for age, waist circumference and smoking; Model 2 included additional adjustment for diabetes, hypertension and ACVD; and, finally, Model 3 included adjustment for socio‐economic conditions, education years and toothbrushing frequency.
The level of statistical significance was set at p < .05. Analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA), Stata release 17 (Stata Corp., College Station, SA), and R (R Core Team, Vienna, Austria).
Multiple linear regression was conducted in a sequential model design where potential confounding variables were added to produce a fully adjusted final model incorporating established predictors of % predicted FEV1. Model 1 included adjustment for age, waist circumference and smoking; Model 2 included additional adjustment for diabetes, hypertension and ACVD; and, finally, Model 3 included adjustment for socio‐economic conditions, education years and toothbrushing frequency.
The level of statistical significance was set at p < .05. Analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA), Stata release 17 (Stata Corp., College Station, SA), and R (R Core Team, Vienna, Austria).
5
Gender Disparities in Childhood Vaccination
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We reported the median and interquartile range (IQR) of the UCs for the M:F ratios, along with the ranges at the UC level. UCs with no children vaccinated for any particular vaccine were excluded from the analysis for that particular vaccine only. A male-to-female ratio of 0.00 indicated that there were no males or females vaccinated in the particular UC. This was due to the reduced population sizes when we examined our indicators across the sub-categories (maternal literacy and geographic location of vaccination) within a UC.
For our secondary outcome, we computed the GIR by dividing the proportion of males who were due and received vaccinations by the proportion of females who were due and received vaccinations. A GIR of 1.00 implied no differential in coverage rates between females and males, whereas a GIR of above 1.00 indicated inequalities (with higher coverage rates for males relative to females). We performed statistical analyses with Stata, release 17 (StataCorp, College Station, TX, USA). We used digital maps to review the immunization coverage by the district and UC using QGIS (3.16.7-Hannover).
For our secondary outcome, we computed the GIR by dividing the proportion of males who were due and received vaccinations by the proportion of females who were due and received vaccinations. A GIR of 1.00 implied no differential in coverage rates between females and males, whereas a GIR of above 1.00 indicated inequalities (with higher coverage rates for males relative to females). We performed statistical analyses with Stata, release 17 (StataCorp, College Station, TX, USA). We used digital maps to review the immunization coverage by the district and UC using QGIS (3.16.7-Hannover).
6
Bone Health in Extremely Preterm Infants
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Data were analyzed using Stata Release 17 (StataCorp, College Station, TX, USA). Participant characteristics were compared between EP/ELBW and control participants using linear regression for continuous variables or logistic regression for categorical variables. Bone variables were compared between EP/ELBW and controls using linear regression models fitted with generalized estimating equations and reported with robust (sandwich) estimation of standard errors to account for lack of independence within multiple births from the same family. We reported results both unadjusted and adjusted for height and weight to evaluate the impact of body size on the variables. To determine whether between‐group differences varied between the sexes, we added an interaction term for group‐by‐sex; if there was evidence for an interaction (p < 0.05), we reported differences between EP/ELBW and control groups within each sex separately. We acknowledge the multiple comparisons and have interpreted our findings by focusing on the strength of the evidence for overall patterns and magnitude of differences, rather than on individual p values. With sample sizes of 162 EP/ELBW and 122 controls, we have 80% power to find a difference in means between the two groups as small as one‐third of a SD.
7
Determinants of Vaccine Hesitancy: A Statistical Analysis
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We performed analyses using Stata release 17 (StataCorp). We used basic descriptive statistics to characterize the sample and study variables. We assessed univariate predictors of vaccine hesitancy with cross-tabulations and the Fisher exact test, with P < .05 considered significant. We estimated effect sizes using Cramer V, which is a measure of the strength of association between 2 categorical variables and has a range of 0 (no correlation) to 1 (perfect correlation). We used multivariate regression analysis to assess independent effects of variables on vaccine hesitancy. We reported 95% CIs for percentages and odds ratios. We reported the z score for the test of the null hypothesis that the odds ratios did not differ significantly from the reference group. We used Cronbach α, which ranges from 0 to 1.0, to assess the internal consistency (reliability) of the items composing each of the 3 multiple-item scales. An internally consistent scale is one in which much of its variance is variance that is shared (covariance) by the items used to measure it.27 (link)
Cronbach α values are sensitive to the number of items used, but in general, values of 0.70, 0.80, and 0.90 indicate acceptable, good, and excellent reliability, respectively.
Cronbach α values are sensitive to the number of items used, but in general, values of 0.70, 0.80, and 0.90 indicate acceptable, good, and excellent reliability, respectively.
8
Evaluating Indoor Environmental Quality Impacts on Health
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All data will be tested for normal distribution. To analyse how IAQ, perception of IEQ, building characteristics and crowding are associated with general health symptoms data will be analysed by linear or logistic regression. Mean within individual changes will be tested with paired T-test and unpaired for group differences. In case of not normally distributed data we apply Wilcoxon Rank Sum test or McNemars test for ordinal data and Wilcoxon Sign Rank test for nominal data. The same approach is used for follow-up data and results compared to baseline results to investigate changes. The level of statistical significance is defined at P < .05. The statistical software program Stata (Release 17) is used for analyses.48
The covariates for data analyses are expected to be determined by either the use of Directed Acyclic Graphs (DAGs),49 (link)
a priori, and from existing literature.
Estimated power was calculated from a two-sample paired – proportions test and Large-sample McNemar’s test (Table 5 ). Power estimation was based on alpha (0.05), number (195), delta (0.1000), p12 (2%) and p21 (12%). The power was estimated to be 96.70. A 30% participation rate was expected (n = 431) before the data collection prior to the renovation, and a drop-out rate of 50% at follow-up after renovation (n = 216).
The covariates for data analyses are expected to be determined by either the use of Directed Acyclic Graphs (DAGs),49 (link)
a priori, and from existing literature.
Estimated power was calculated from a two-sample paired – proportions test and Large-sample McNemar’s test (
9
Alzheimer's Disease Immunohistochemical Markers
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For each annotated region, between-group differences according to each marker were quantified using Kruskal-Wallis tests. Variable transformations (e.g., natural log) were used as necessary. A Spearman’s correlation coefficient between each immunohistochemical marker and each AD endpoint (NFT stage, Aβ plaque load, MMSE) was calculated. Graphs showing individual data points in each group and their relationships to sAD endpoints are provided in Figs. 3 –4 , 6 , 9 , 10 . In a sensitivity analyses accounting for the false discovery rate, we adjusted the p-values using the two-stage linear step-up procedure described by Benjamini et al.(56 , 57 ) (presented in Suppl Table 4 ). Statistical analyses were conducted in Stata Release 17.(58 )
10
Stata 17 Data Analyses
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Stata (Release 17. College Station, TX: StataCorp LLC) was used for all data analyses.
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