We calculated descriptive statistics using Stata SE statistical software version 17.0 (StataCorp) and R statistical software version 4.2.3 (R Project for Statistical Computing) using 2-tailed 95% CIs and hypothesis tests. Statistical significance was set at P < .05. For comparison of the sample to the sampling frame of all nonfederal acute care hospitals, χ2 tests were used. For comparisons within the sample, survey statistics were used (R survey package version 4.2) to allow for finite population correction.31 (link) For binary variables, the survey-weighted Rao-Scott scaled χ2 distribution for the loglikelihood from a binomial distribution was used. Where either exactly 0% or 100% of sampled privacy policies contained an element, the Clopper-Pearson exact 95% CI was used.
Stata se statistical software version 17
Manufactured by StataCorp
Stata SE is a statistical software package developed by StataCorp. Version 17.0 provides a comprehensive suite of data analysis and management tools for researchers and professionals. The software offers advanced statistical modeling, data visualization, and reporting capabilities.
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Lab products found in correlation
4 protocols using stata se statistical software version 17
1
Descriptive Statistics in Acute Care Hospitals
2
Anemia's Impact on Sickle Cell Pregnancy
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In secondary analyses, we assessed the contributions of anemia to SMM and other APOs among pregnant people with SCD. We used rates to calculate the relative risk (RR) of outcomes in SCD vs anemia delivery groups.
Analyses were performed with Stata SE statistical software version 17.0 (StataCorp).18 Missing data were handled with listwise deletion for each model in accordance with standards issued by data set administrators and implemented in previous studies using the NIS.19 (link) Information about missing data is included in eTable 3 inSupplement 1 . Statistical tests were survey weighted. To minimize the risk of false discovery, we applied the Benjamini and Yekutieli correction20 (link) across analyses and used a tolerable false discovery rate of less than 5%. All reported P values are 2-sided and corrected; when a comparison is reported as significant, the P value is less than .05 after multiple comparisons correction. Data were analyzed from September 2021 to August 2022.
Analyses were performed with Stata SE statistical software version 17.0 (StataCorp).18 Missing data were handled with listwise deletion for each model in accordance with standards issued by data set administrators and implemented in previous studies using the NIS.19 (link) Information about missing data is included in eTable 3 in
3
Substance Use Disorder Service Availability in Safety-Net Hospitals
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Data analysis was performed from January to March 2022. In descriptive analyses, we calculated the mean value of organizational and county variables, including the availability of SUD services, by SNH and non-SNH groups, and compared these values using t tests or χ2 tests. Next, we estimated 5 separate multilevel logistic regression models that accounted for the clustering of hospitals in states to examine differences in the availability of SUD services by safety-net status after controlling for organizational and environmental covariates. In secondary analysis, we ran separate regression models for screening and consultation, stratified by treatment setting, and MOUD, inpatient, and outpatient services stratified by treatment entity. Finally, we conducted subgroup analyses by estimating similar multilevel logistic regression models by the 3 ownership types. A 2-sided P < .05 calculated by the χ2 tests, t tests, and multivariable regression was considered statistically significant. Analyses were computed with Stata SE statistical software version 17 (StataCorp).36
4
Trends in Oral Anticoagulant Usage and Costs
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For patients’ out-of-pocket costs for drugs, insurance drug payments, and initiation of OAC treatment rates, the first year was defined as July 2014–June 2015, and the same for the following years, where July 2020–June 2021 was the last year from July 2014 to June 2021. As supplementary analyses, authors reported (1) percent of patients with zero out-of-pocket costs or zero insurance payments; (2) median patients’ out-of-pocket costs and insurance payments; and (3) relative volumes of OACs.
Patient characteristics such as age at first AF diagnosis, sex, urban/rural residency, census region, race, and ethnicity (only available to individuals in Medicaid), and the number and proportion of patients who had OAC drug claims and total drug payments within 90 days of the first AF diagnosis by the OAC agents were summarized by insurance types.
For trend analysis, Joinpoint regression models (version 4.9.0.0, National Cancer Institute) were used to test the yearly trends of the proportions of patients treated with OAC. The average annual percentage change (AAPC) was calculated, which showed the average measure of total annual percentage changes for the entire study period (from July 2014 to June 2021). All other analyses were conducted using Stata SE statistical software version 17 (StataCorp, College Station, TX). Data analyses were performed in 2022–2023.
Patient characteristics such as age at first AF diagnosis, sex, urban/rural residency, census region, race, and ethnicity (only available to individuals in Medicaid), and the number and proportion of patients who had OAC drug claims and total drug payments within 90 days of the first AF diagnosis by the OAC agents were summarized by insurance types.
For trend analysis, Joinpoint regression models (version 4.9.0.0, National Cancer Institute) were used to test the yearly trends of the proportions of patients treated with OAC. The average annual percentage change (AAPC) was calculated, which showed the average measure of total annual percentage changes for the entire study period (from July 2014 to June 2021). All other analyses were conducted using Stata SE statistical software version 17 (StataCorp, College Station, TX). Data analyses were performed in 2022–2023.
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