The aims of the study were tested with logistic and linear regression. The first aim was tested with binomial logistic regression with the logit command in STATA 14.1 (44 ). The independent variable (IV) was pre-treatment sleep disturbance group (0=no sleep disturbance, 1=insomnia symptoms, 2= hypersomnia symptoms, 3=both insomnia and hypersomnia symptoms) and the dependent variable (DV) was primary substance use problem at pre-treatment. Given that the primary substance use problem variable does not have a natural reference group for comparisons (e.g., “no substance use”), the variable was coded as follows: 0=cocaine, 1=alcohol; 0=cocaine, 1=heroin; or 0=heroin, 1=alcohol. The second aim was tested with linear regression with the regress command from STATA 14.1 (44 ). The IV was pre-treatment sleep disturbance group and the DV was pre-treatment substance use frequency. The third aim was tested with a binomial logistic regression with the logit command from STATA 14.1 (44 ). The IV was pre-treatment sleep disturbance group and the DV was substance use 12-months post-treatment (0=absence, 1=presence) for alcohol, cocaine, and heroin. All models also included age, sex, race, age, education level, and depression history assessed at pre-treatment as covariates. Analyses for the third aim also included treatment modality and in-treatment substance use as covariates.
Stata 14
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Lab products found in correlation
Sas 9, by SAS Institute (89 mentions)
Sas version 9, by SAS Institute (29 mentions)
Prism 7, by GraphPad (28 mentions)
Stata statistical software release 14, by StataCorp (25 mentions)
Prism 8, by GraphPad (22 mentions)
Sas 9, by StataCorp (15 mentions)
Spss 22, by IBM (13 mentions)
Epidata version 3, by StataCorp (13 mentions)
Prism 6, by GraphPad (13 mentions)
Prism 5, by GraphPad (12 mentions)
Stata statistical software, by StataCorp (11 mentions)
Stata, by StataCorp (11 mentions)
Revman 5, by StataCorp (9 mentions)
Sas software, by SAS Institute (8 mentions)
R version 3, by R Project for Statistical Computing (7 mentions)
Epidata 3, by StataCorp (6 mentions)
Sas v9, by SAS Institute (6 mentions)
Spss 23, by IBM (5 mentions)
Excel 2013, by StataCorp (5 mentions)
Stata version 14, by StataCorp (5 mentions)
5 024 protocols using stata 14
1
Sleep Disturbances and Substance Use
2
Statistical Methods for Heterogeneous Data
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We analyzed the data using Review Manager 5.4 https://en.freedownloadmanager.org/Windows-PC/Review-Manager.html and Stata 14 https://www.stata.com/stata14/ . The binary categorical variable data results are presented as the risk ratios (RR) with 95% CI and standard mean difference (SMD) with the 95% confidence interval (CI) in continuity variable data. I2 statistics were used for the heterogeneity assessment. In the absence of significant data heterogeneity (I2 > 50%), a model with fixed effects was used. However, considerable heterogeneity was observed (I2 > 50%), subgroup analysis, meta-regression, and sensitivity analysis can be performed to eliminate heterogeneity. If heterogeneity persists but clinically suggests homogeneity (patient age, sex, course, underlying condition, etc, baseline conditions are substantially consistent between groups), random-effects model analysis was used. Finally, publication bias was evaluated using funnel charts and Egger tests in Stata 14.
3
Meta-analysis of Vascular Markers
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Statistical analyses were performed using Review Manager (RevMan) 5.3.5 software (Cochrane Community, London, United Kingdom) and STATA 14 software (STATA Corp., College Station, TX). Dichotomous data of the clinicopathological features were pooled using odds ratios (ORs) with 95% confidence intervals (CIs). HRs were pooled as inverse variance data with 95% CI. P < 0.05 was considered to indicate a statistically significant difference. An observed HR or OR > 1 implied a worse prognosis for the group with VM positivity, and it was considered statistically significant if the values of 95% confidence intervals did not overlap the value “1.” The heterogeneity of the included studies was evaluated by the χ2 and I2 tests, and P < 0.10 or I2 > 50% was defined as heterogeneous. The fixed-effect model was used for merging the homogeneous data, and the random-effects model was suitable for merging the heterogeneous data as previously reported [26 (link)]. Publication bias was evaluated by Egger's test (STATA 14) with P < 0.05 indicating potential bias. The sensitivity analysis was evaluated by reanalyzing the data using different statistical approaches.
4
Meta-analysis of Biomarker Association
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We utilized Review manager 5.3 and Stata 14.0 software to perform the meta-analysis in the present study. Heterogeneity among studies was assessed by the I2 statistic, and P<0.10 and I2 > 50% indicated evidence of heterogeneity. If heterogeneity existed among the studies, a random-effects model was used to estimate the pooled standard mean difference (SMD). Otherwise, a fixed-effects model was adopted. The SMD and corresponding 95% confidence interval (CI) were utilized to assess the associations. Subgroups analysis regarding geographical area, median of detection of patients, sample size, with EDTA anticoagulation treatment and NOS quality, were also performed to explore source of heterogeneity. Egger’s and Begg’s tests (P<0.05) can demonstrate a statistically significant publication bias and were conducted with the Stata 14.0 software, if there was any publication bias, trim, and fill method was implemented. Sensitive analysis was also conducted by changing effect model.
5
Meta-Analysis Statistical Techniques
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In this study, Review Manager 5.3 (The Cochrane Collaboration, Copenhagen, Denmark) and STATA 14.0 software (STATA Corporation, College Station, TX, USA) were used to work with data. The standardized mean value (SMD) with 95% confidence interval (CI) was used as the effect measure to compare consecutive results from different scales to calculate each study. The point estimates associated with the summary results for each study were shown in forest plots. Heterogeneity of meta-analysis was evaluated by I2 statistic. And I2 statistic with values >50% indicated significant heterogeneity in this study. SMD was combined with 95% CI by using random effect model with heterogeneity or fixed effect model without heterogeneity. If heterogeneity existed, a sensitivity analysis was performed to investigate the impact of each study on the overall estimate. Publication bias was detected by the funnel plots using STATA 14.0 software.
6
Meta-Analysis Methodology for Standardized Outcomes
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Meta-analysis was performed using the Review Manager software (Vision 5.3, The Cochrane Collaboration, Oxford, United Kingdom). We used Hedge's g standardized mean differences (SMDs) as a measure of effect size because the outcomes of the studies were frequently measured by different assays and techniques. SMD becomes dimensionless and the scales become uniform across the different studies. Results were given as SMD and 95% confidence intervals (95%CI).
The statistical heterogeneity was determined by the value of the I2 statistic using Hedge's test. When the statistical heterogeneity was notable (I2 > 50%), an integrated effect was calculated with a random-effect model; otherwise, the fixed-effect model was used.
Publication bias was estimated by funnel plot asymmetry and Egger's regression test performed by Stata 14.0 software, when 10 or more studies were included in the same analysis [24 (link)]. A sensitivity analysis was also performed with Stata 14.0 software to evaluate the robustness in the conclusion of each analysis with over 10 or more studies [25 (link)]. All P values were evaluated using two-tailed tests, and p < 0.05 was set as statistically significant.
The statistical heterogeneity was determined by the value of the I2 statistic using Hedge's test. When the statistical heterogeneity was notable (I2 > 50%), an integrated effect was calculated with a random-effect model; otherwise, the fixed-effect model was used.
Publication bias was estimated by funnel plot asymmetry and Egger's regression test performed by Stata 14.0 software, when 10 or more studies were included in the same analysis [24 (link)]. A sensitivity analysis was also performed with Stata 14.0 software to evaluate the robustness in the conclusion of each analysis with over 10 or more studies [25 (link)]. All P values were evaluated using two-tailed tests, and p < 0.05 was set as statistically significant.
7
Genome-wide Genetic Analysis of Cardiometabolic Traits
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For Observational study, all these statistical analyses were conducted in R version 4.0.3 and STATA 14.1 software. A Bonferroni-corrected threshold of p < 0.05 was considered statistically significant.
The GWAS analysis of BMI, WCadjBMI and HCadjBMI was performed using PLINK software (http://www.coggenomics.org/plink2 ), and we used the option --clump-r2 and --clump -kb to obtain the independent locus.
The wGRS method was performed using PLINK software (http://www.coggenomics.org/plink2 ) with the command of --score sum to obtain the sum of valid per-allele scores (Chang et al., 2015 (link)). The asterisks indicate significant differences between groups (∗ = p < 0.05; ∗∗ = p < 0.01). The different letters above each of plots indicate significant differences according to Chi-Square test. The Chi square test for quartile groups was conducted in STATA 14.1 software.
For Two-sample MR, all statistical analyses were performed with R 4.0.3. The IVW, simple mode, weighted mode, weighted-median, and MR-Egger methods were performed using the “MendelianRandomization” package (Yavorska and Burgess, 2017 (link)). The MR-PRESSO approach was performed using the “MR-PRESSO” package (Verbanck et al., 2018 (link)). The two-sided p value of less than 0.05 was considered statistically significant.
The GWAS analysis of BMI, WCadjBMI and HCadjBMI was performed using PLINK software (
The wGRS method was performed using PLINK software (
For Two-sample MR, all statistical analyses were performed with R 4.0.3. The IVW, simple mode, weighted mode, weighted-median, and MR-Egger methods were performed using the “MendelianRandomization” package (Yavorska and Burgess, 2017 (link)). The MR-PRESSO approach was performed using the “MR-PRESSO” package (Verbanck et al., 2018 (link)). The two-sided p value of less than 0.05 was considered statistically significant.
8
Meta-analysis of HIF-1α Expression
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For the acquired microarrays, the researchers extracted data concerning the expression of HIF‐1α, calculated the mean and SD values, and employed Stata 14.0 for meta‐analysis. If P < .05 and I2 > 50%, it was determined that heterogeneity existed, and the random effects model was used. If P ≥ .05 and I2 ≤ 50%, it was determined that homogeneity existed, and the fixed effects model was utilized. Subsequently, forest maps and funnel plots were used to illustrate the analysis. Stata 14.0 was employed to carry out the meta‐analyses of measuring the summary receiver operating characteristic (SROC), helping the researchers draw SROC curves.
9
Dietary Intake Assessment via 24-Hour Recall
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First, participants were asked to describe all foods and beverages consumed
during the previous day from their wake-up time until their last meal, without
interruptions by the interviewer. Subsequently, the interviewer requested a
detailed description of each food and beverage reported, meal time, preparation,
food brand, and portion sizes. Finally, a review of all reported items was made
(20 (link)). To reduce recall biases and to
assist in the identification of the estimated portion size, the interviewers
used a photo album with pictures of the home utensils and food portions as
support material (21 ).
The software STATA 14.0 (<https://www.stata.com/stata14/ >; StataCorp, USA) was used to
analyze the food consumption data from the 24HR and the FFQ. Portion size was
converted into grams or milliliters using a standard reference table (22 ).
The energy and nutrient intake were estimated using the Brazilian Food
Composition Table (23 ), complemented with
the United States Department of Agriculture (USDA) National Nutrient Database
for Standard Reference (24 ).
during the previous day from their wake-up time until their last meal, without
interruptions by the interviewer. Subsequently, the interviewer requested a
detailed description of each food and beverage reported, meal time, preparation,
food brand, and portion sizes. Finally, a review of all reported items was made
(20 (link)). To reduce recall biases and to
assist in the identification of the estimated portion size, the interviewers
used a photo album with pictures of the home utensils and food portions as
support material (21 ).
The software STATA 14.0 (<
analyze the food consumption data from the 24HR and the FFQ. Portion size was
converted into grams or milliliters using a standard reference table (22 ).
The energy and nutrient intake were estimated using the Brazilian Food
Composition Table (23 ), complemented with
the United States Department of Agriculture (USDA) National Nutrient Database
for Standard Reference (24 ).
10
Statistical Analysis Methodology for Research Protocols
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The software Review Manager 5.3 and Stata14.0 were used for statistical analysis. The odd ratio (OR) and 95% confidence interval (95%CI) were used as the combined effect size indicators for taxonomic variables, and standardized mean difference (SMD) was used for continuous variables. SMD and 95%CI as pooled effect size indicators. The Q test and I2 were used to assess the heterogeneity among the studies. For included studies with no or low heterogeneity (I2 < 50%, P > .1), fixed-effect models were used for analysis. If the heterogeneity of included studies was high (I2 ≥ 50%, P < .1), the random effects model (REM) was used for analysis. Subgroups analyzed the factors that may lead to heterogeneity. A funnel plot was drawn by Stata14.0 software, and publication bias was analyzed. Whether there is, the P value represents statistical significance. When P < .05, the difference is considered significant; otherwise, there is no considerable significance.
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