Independent‐samples t‐test was performed to compare the expression status of MAOA between the HCC and non‐cancer groups using GraphPad Prism v8.0 software. To assess the capacity of MAOA to distinguish HCC patients from people without liver cancer, the receiver operating characteristic (ROC) curve was plotted using GraphPad v8.0 software. The area under the curve (AUC)> 0.70 was recognized to have moderate diagnostic capability, and AUC >0.90 indicated great diagnostic capability. Subsequently, the standard mean difference (SMD) was computed to assess the general expression level of MAOA in HCC patients using STATA v12.0 software. To evaluate the heterogeneity of the included datasets, we performed a heterogeneity test in STATA v12.0, where I2 >0.50 indicated significant heterogeneity, and a random effect analysis was required. A Begg's funnel plot was drawn to determine whether publication bias occurred, and a P‐value ≥.05 indicated no publication bias. A summary of the receiver operating characteristic (sROC) curve was presented, and the specificity and sensitivity of MAOA were computed in STATA v12.0. A Kaplan‐Meier curve was plotted to obtain further insight into the association between the MAOA expression level and the overall survival condition of HCC patients.
Stata v 12
Manufactured by StataCorp
Sourced in United States, United Kingdom, Denmark
Stata V.12 is a software package designed for statistical analysis, data management, and graphics. It provides a comprehensive set of tools for researchers, analysts, and professionals in various fields. The core function of Stata V.12 is to facilitate the analysis of data, including the ability to perform a wide range of statistical tests, create graphs and visualizations, and manage complex datasets.
Automatically generated - may contain errors
Lab products found in correlation
Sas v9, by SAS Institute (8 mentions)
Prism v6, by GraphPad (7 mentions)
Stata statistical software, by StataCorp (5 mentions)
Spss v20, by IBM (5 mentions)
Prism 5, by GraphPad (5 mentions)
Sas v9, by StataCorp (4 mentions)
Revman v5, by StataCorp (3 mentions)
Spss v19, by IBM (2 mentions)
Spss v18, by IBM (2 mentions)
Spss v22, by IBM (2 mentions)
Prism v8, by GraphPad (2 mentions)
Prism 8, by GraphPad (2 mentions)
Stata statistical software release 12, by StataCorp (2 mentions)
Excel 2010, by StataCorp (2 mentions)
Spss v 25, by StataCorp (2 mentions)
Spss v 21, by StataCorp (2 mentions)
Prism v5, by GraphPad (2 mentions)
Mastercycler nexus, by Eppendorf (1 mentions)
Qiaamp virus biorobot 9604 kit, by Qiagen (1 mentions)
Bio plex magpix multiplex reader, by DiaSorin (1 mentions)
730 protocols using stata v 12
1
MAOA Expression in Liver Cancer
2
Statistical Analysis of Interventional Outcomes
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RevMan v5.3 and Stata v12.0 were utilized for all statistical analyses. The pooled odds ratios (ORs) and 95% confidence intervals (CIs) for dichotomous variables were calculated using the Mantel-Haenszel approach, while pooled estimates of the mean difference (MD) with 95% CIs were determined for continuous variables. Stent patency and OS between groups were compared using hazard ratios (HRs) with 95% CIs. Heterogeneity among the included studies was gauged using the χ2 test and the I2 statistic (I2 > 50% indicating significant heterogeneity). Fixed-effects models were used in the absence of any significant heterogeneity. Sources of potential heterogeneity were identified via sensitivity analysis. The pooled clinical response rates were calculated using Stata v12.0. Funnel plots were used to analyse the potential publication bias.
3
Meta-analysis of Genetic Association Studies
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All analyses were carried out using Stata v12.0 (StataCorp LP, College Station,
TX, USA). Heterogeneity among the study results was assessed by χ2and I2 tests, and appropriate analysis models (fixed-effect or
random-effect) were determined; a χ2P ≤ 0.05 and an I2 > 50% indicated high
heterogeneity and a random-effects model was used in this case, while a
χ2P > 0.05 and an I2 ≤ 50% indicated acceptable
heterogeneity and a fixed-effects model was used. Egger’s and Begg’s tests were
used to detect publication bias. If the Hardy–Weinberg equilibrium (HWE) genetic
balance test was not reported in the original text or was not performed in the
control group, we carried out manual detection using Stata v12.0 and extracted
the corresponding results (P value). The meta-analysis was
carried out using five commonly used gene models: allelic model (a vs b);
homozygote model (aa vs bb); heterozygote model (ab vs bb); dominant model
(aa + ab vs bb); and regressive model (aa vs ab + bb). Odds ratios (ORs) and 95%
confidence intervals (CIs) were used to analyze all the indexes. We also
performed subgroup analyses according to overall,10 (link)23 (link, link, link, link, link, link, link, link, link, link, link, link, link) mixed,12 (link),16 (link),19 (link),21 (link)Caucasian,10 (link),14 (link),18 (link),22 (link) Black,18 (link)
Asian,15 (link),20 (link),23 (link) and HWE.
TX, USA). Heterogeneity among the study results was assessed by χ2and I2 tests, and appropriate analysis models (fixed-effect or
random-effect) were determined; a χ2P ≤ 0.05 and an I2 > 50% indicated high
heterogeneity and a random-effects model was used in this case, while a
χ2P > 0.05 and an I2 ≤ 50% indicated acceptable
heterogeneity and a fixed-effects model was used. Egger’s and Begg’s tests were
used to detect publication bias. If the Hardy–Weinberg equilibrium (HWE) genetic
balance test was not reported in the original text or was not performed in the
control group, we carried out manual detection using Stata v12.0 and extracted
the corresponding results (P value). The meta-analysis was
carried out using five commonly used gene models: allelic model (a vs b);
homozygote model (aa vs bb); heterozygote model (ab vs bb); dominant model
(aa + ab vs bb); and regressive model (aa vs ab + bb). Odds ratios (ORs) and 95%
confidence intervals (CIs) were used to analyze all the indexes. We also
performed subgroup analyses according to overall,10 (link)
Asian,15 (link),20 (link),23 (link) and HWE.
4
Molecular Biomarker Detection Methodology
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Stata v12.0 was used to calculate the pooled adequacy rates for molecular tests, positive rates of EGFR mutations, ALK translocation, and KRAS mutations. The random-effects model was generated. Heterogeneity was calculated using the Q test and the I2 statistic, with I2 > 50% being indicative of significant heterogeneity. The sources of heterogeneity were detected using meta-regression, subgroup analysis, and sensitivity analysis. The subgroup analyses were performed based on the different needle types (fine or core needles) used, mean lesion sizes (< or ≥ 4 cm), and countries (Asian or Western). Additionally, the Egger test was used to evaluate the potential risk of publication bias by Stata v12.0. A high risk of publication bias was considered if the p-value was > 0.05.
5
Meta-analysis of Symptom-onset to Blood Collection
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Statistical analysis was implemented using Review Manager V.5.2 (The Nordic Cochrane Centre, the Cochrane Collaboration, Copenhagen, Denmark). The combined effect of mean difference (MD) and 95% CI was defined by a random-effect model or a fixed-effect model. The heterogeneity among included studies was tested using the inconsistency index (I2). A random-effect model was used when there was a high level of heterogeneity (I2 values>50%). A p<0.05 was considered statistically significant. For indicators with higher heterogeneity, sensitivity analysis and subgroup analyses were carried out to identify the reliability of the meta-analysis and meta-regression was used to explore the sources of heterogeneity. Publication bias was evaluated qualitatively by visual observation of funnel graphs and was assessed quantitatively by the Begg’s test (Stata V.12.0), which manifests a publication bias with p<0.05. The number, MD, SD of the experimental group and control group of the included studies and country, sample size, male, the time from symptom onset to blood collection of 17 included studies were imported into Stata V.12.0 software.
6
Statistical Analysis of Genetic Models
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STATA v12.0 (TX, USA) was used for all statistical analyses. Heterogeneity in the study
results was assessed using chi-squared and I2 tests, and appropriate analysis models (fixed-effect or random-effect) were determined. A
chi-squared p ⩽ 0.05 and an I2 > 50% indicated high
heterogeneity, and thus a random-effects model was used. A chi-squared p > 0.05 and an I2 ⩽ 50% indicated acceptable
heterogeneity, and thus a fixed-effects model was used instead. Egger’s test and
Begg’s test were performed to determine publication bias. On condition that the
Hady Weinberg equilibrium (HWE) genetic balance test was neither provided in the original
text nor performed in the control group, STATA v12.0 was used to obtain corresponding
results (p value). Five commonly used gene models were
selected for this meta-analysis: allelic model (D vs I), homozygote model (DD vs II),
heterozygote model (DI vs II), dominant model (DD + DI vs II), and
regressive model (DD vs DI + II). All the indexes and statistics were
analyzed by OR and 95%CI.
results was assessed using chi-squared and I2 tests, and appropriate analysis models (fixed-effect or random-effect) were determined. A
chi-squared p ⩽ 0.05 and an I2 > 50% indicated high
heterogeneity, and thus a random-effects model was used. A chi-squared p > 0.05 and an I2 ⩽ 50% indicated acceptable
heterogeneity, and thus a fixed-effects model was used instead. Egger’s test and
Begg’s test were performed to determine publication bias. On condition that the
Hady Weinberg equilibrium (HWE) genetic balance test was neither provided in the original
text nor performed in the control group, STATA v12.0 was used to obtain corresponding
results (p value). Five commonly used gene models were
selected for this meta-analysis: allelic model (D vs I), homozygote model (DD vs II),
heterozygote model (DI vs II), dominant model (DD + DI vs II), and
regressive model (DD vs DI + II). All the indexes and statistics were
analyzed by OR and 95%CI.
7
Actigraphy Validation in Sleep Apnea
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The study was analyzed as intention to treat analysis. Sample size was calculated from arousal index using mean difference between two independent samples derived from Huang et al. paper.17 (link) We used α = 0.05, and β = 0.20, and calculated to have 6 patients in each arm (total of 12 patients). We also added 30% for dropout rate and another 30% for potential uninterpretable data. Eventually, a total of 20 patients were planned. Quantitative variables were compared between two groups using t test or Mann–Whitney U test and linear regression analysis. Chi-squared or Fisher's exact test was used to compare proportion between groups. Correlation analysis between polysomnography and Actiwatch parameters was analyzed using Lin's concordance correlation coefficient (0.21–0.40 = fair, 0.41–0.60 = moderate, 0.61–0.80 = substantial, and 0.81–1.00 = almost perfect). STATA v12.1 software was utilized. Descriptive analysis was used for analysis. This study was approved by the Ethics Committee. The study was registered at www.clinicaltrials.in.th (#TCTR20170727003).
8
Inflammatory Biomarkers in Obstructive Sleep Apnea
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Data are summarised as frequencies for categorical variables and median (interquartile range) for continuous variables. Baseline data for the groups studied were compared using Chi-squared, analysis of variance and Kruskal-Wallis tests, as appropriate. Continuous variables of inflammatory biomarkers in plasma and PHAL were normally distributed and log-transformed. Pearson's correlation coefficients were calculated adjusting for age, sex, body mass index (BMI), ESS and AHI. The difference between baseline and 1-year follow-up of biomarkers was assessed using the Wilcoxon signed-rank test or paired t-test, as appropriate. All analyses were repeated by separating the population into two groups according to BMI: ⩽30 kg•m -2 versus ⩾30 kg•m -2 . Statistical analyses were performed with the STATA v.12.1 software (College Station, TX, USA).
9
Evaluating Prescribing Changes Post-KW Introduction
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Regression models were used to assess changes in prescribing after the introduction of KW for practices implementing wave 1 of the programme and for non-KW practices. ARIMA error models were created for each drug category to account for autocorrelation and seasonality in the data.18 In further analyses, we examined the possibility of a variation of the intervention effect between different health boards by including interaction terms. Data from 66 wave 1 KW practices were included for the additional analyses (data from practices in NHS Tayside did not include practice codes and could not be matched by ISD to the practice-level data on prescription rates and so were excluded). Generalised estimating equations (GEEs) were used to analyse prescribing data over time for practices in the remaining three health board areas. The quasi-likelihood under the independence model criterion (QIC) statistic informed model selection.19 All analyses were undertaken using Stata V.12.1 software (Stata Corp, College Station, Texas, USA; http://www.stata.com ). Ethical approval was not sought since the study used aggregated data.
10
Comprehensive Viral Panel Screening Protocol
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The samples were tested for 16 different viruses [adenovirus, coronavirus (NL63, OC43, HKU1, 229E), enterovirus/rhinovirus, human metapneumovirus, human bocavirus, influenza virus type A (subtype H1N1, subtype H3), influenza virus type B, parainfluenza virus (PIV-1, PIV-2, PIV-3, PIV-4), respiratory syncytial virus (RSV)] using the Luminex xTag RVP (respiratory virus panel) assay (Luminex, Molecular Diagnostics, Canada) according to the manufacturer's instructions [11] . Total nucleic acid was extracted from the swabs using the QIAamp Virus BioRobot 9604 kit (Qiagen, Germany), employed on a Microlab Star Hamilton robot (Hamilton Robotics GmbH, Germany). PCR was performed on an Eppendorf Mastercycler Nexus (Eppendorf, Germany) and hybridization/identification carried out on a Bio-Plex MAGPIX Multiplex Reader (Luminex).
Data were analysed using Stata v. 12.1 software (StataCorp., USA) and χ 2 test.
Data were analysed using Stata v. 12.1 software (StataCorp., USA) and χ 2 test.
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