After data collection was complete, we randomly divided the sample in half using a random number generator in Stata SE (Version 16). We used the first half of the data to conduct an exploratory factor analysis (EFA) with an oblique rotation using the “polychoric” command in Stata SE (Version 16) for ordinal factor indicators. We reviewed Eigenvalues, Scree plots, parallel analyses, and factor loadings to determine an underlying factor structure. Given that we needed to reduce the number of items in our scale, we used a strict factor loading cut-off (>0.65) and removed any items that cross-loaded (>0.3) onto more than one factor. We then conducted a confirmatory factor analysis (CFA) using the second half of the data to test the factor structure and assess factor loadings and model fit. Model fit indices and standard cut-off values included: Comparative Factor Index (CFI) >0.9, Tucker-Lewis Index (TLI) >0.9, Root Mean Square Error of Approximation (RMSEA)<0.06, Standardized Root Mean Square Residual (SRMR) <0.05 using weighted least squares mean and variance adjusted estimation and specified ordinal factor indicators in Mplus (Version 8).32 –33 (link)
Stata se
Manufactured by StataCorp
Sourced in United States
Stata/SE is a data analysis and statistical software package developed by StataCorp. It provides a comprehensive set of tools for data management, analysis, and visualization. Stata/SE is designed to handle large datasets and offers a wide range of statistical procedures, including regression analysis, time series analysis, and multilevel modeling.
Automatically generated - may contain errors
Lab products found in correlation
Stata statistical software, by StataCorp (4 mentions)
Spss statistic, by IBM (4 mentions)
Sas software, by SAS Institute (4 mentions)
Statistical software, by GraphPad (2 mentions)
Stata se software, by StataCorp (2 mentions)
Prism 8, by GraphPad (2 mentions)
Spss 22, by IBM (1 mentions)
Software, by MedCalc (1 mentions)
R statistical software, by R Project for Statistical Computing (1 mentions)
Stata 11, by StataCorp (1 mentions)
R software, by R Project for Statistical Computing (1 mentions)
Stata statistical software release 17, by StataCorp (1 mentions)
Prism 6, by GraphPad (1 mentions)
Spss 25, by IBM (1 mentions)
Spss statistics version, by IBM (1 mentions)
Prism 7, by GraphPad (1 mentions)
Version 7, by ATLAS.ti (1 mentions)
Stata statistical software release 14, by StataCorp (1 mentions)
Stata se 12 for windows, by StataCorp (1 mentions)
Spss statistics 23, by IBM (1 mentions)
293 protocols using stata se
1
Scale Development and Validation
2
Tracking COVID-19 Susceptibility Trajectories
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Descriptive statistics (means with standard deviations and percent frequencies) were calculated for all participant characteristics and survey responses. We then identified groups of individuals following similar progressions of perceived susceptibility to COVID-19 over multiple waves and classified them into trajectory groups using the traj command in Stata/SE, version 15 (StataCorp, College Station, TX, US).[8 ] This method estimates discrete mixture models on longitudinal data, in our case assuming a Bernoulli distribution (logistic model) for the dichotomous perceived susceptibility variable. We used the Bayesian information criterion to determine the number of discrete trajectories in the data. Participants were assigned to a trajectory based on posterior probabilities of belonging to each group.[9 ] Associations between participant characteristics and their assigned trajectory group were examined in bivariate analyses using chi-square tests. A multivariable Poisson model was used to estimate relative risks (with 95% confidence intervals (CIs)) of following a certain trajectory.[10 (link)] Models adjusted for potential confounders (age, gender, race, income, health literacy, employment status, and primary care setting), as well as parent study. All statistical analyses were performed using Stata/SE, version 15 (StataCorp).
3
Predicting Cancer Treatment Response
4
Prognostic Factors in Oncology Outcomes
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Continuous variables were summarized as mean±standard deviation or median (IQR) and were compared across groups using the Kruskal–Wallis test. Categorical variables were analyzed using the Chi-squared test. The relationships between clinical and pathological factors and long-term PFS and OS were assessed using univariate Log rank tests and a multivariate Cox proportional hazard model. Tumor or treatment characteristics that achieved a P-value<0.10 in univariate analysis were included in the multivariate analysis. A correlation matrix was used to examine parameters with high collinearity. Correlation coefficients of >0.4 were considered as showing a positive correlation between variables. Continuous covariates were dichotomized or categorized if necessary to reduce multicollinearity. To model the nonlinear relationship between TTS and OS (or PFS), a restricted cubic spline (RCS) procedure was fitted with three internal knots (10th, 50th, and 90th centiles as suggested by Harrell).18 Testing for trends can be applied based on various statistical hypothesis when necessary. For all analyses, P<0.05 was considered statistically significant. Statistical analyses were performed using SE STATA (Stata Statistical Software, release 15.1; Stata Corp, College Station, TX, USA).
5
Survival Analysis of Cancer Outcomes
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The relationships between clinicopathological factors and OS/PFS were assessed using the Kaplan-Meier method (log-rank test). The median follow-up time was estimated using the reverse Kaplan Meier method. Cox proportional hazards models were used to determine hazard ratios (HR) in univariate and multivariate analyses. To achieve accurate statistical analysis, the rule of thumb is 10 events per variable (EPV) in Cox regressions, according to Peduzzi et al. (18 (link)). Multivariate tools were used to assess cancerous growths and therapeutic achievements of statistical significance (P<0.05) as realized by univariate analysis, in which, a P<0.05 was regarded of statistical significance.
Statistical evaluation was based on SE STATA (Stata Statistical Software, release 15.1; Stata Corp., College Station, TX, USA) or R (R version 3.6.2).
Statistical evaluation was based on SE STATA (Stata Statistical Software, release 15.1; Stata Corp., College Station, TX, USA) or R (R version 3.6.2).
6
Evaluating Tumor Regression Grading Systems for Prognosis
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Continuous variables were summarized as the median (IQR) and were compared across groups using the Kruskal-Wallis test. Categorical variables were analyzed using the Chi-squared test. The relationships between clinical and pathological factors and long-term DFS and OS were assessed using univariate log-rank tests and a multivariate Cox proportional hazard model. Tumor or treatment characteristics that achieved a P value < 0.10 in univariate analysis were included in the multivariate analysis. The prognostic strength and the discrimination ability of each TRG system were assessed using the concordance index (c-index ± SE), with a concordance index of 1 indicating perfect prediction and 0.5 indicating no discrimination. The c-index was calculated and compared using the “survcomp” R package[21 (link)]. Testing for trends was based on various statistical hypotheses when necessary. For all analyses, P < 0.05 was considered to be statistically significant. Statistical analyses were performed using SE STATA (Stata Statistical Software, release 15.1; Stata Corp, College Station, TX, United States).
7
Survival Analysis of Clinical and Pathological Factors
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The associations between clinical and pathological factors and long-term OS were assessed using the Kaplan-Meier method (log-rank test). Cox proportional-hazards models were used to determine hazard ratios (HR) in univariate and multivariate analyses. Tumour or treatment characteristics for which univariate analysis reached values of P<0.05 were tested in multivariate models. To achieve an exact statistical analysis, the rule of thumb is 10 events per variable in the Cox regression. For all analyses, P<0.05 was considered statistically significant.
Statistical analyses were performed using SE STATA (Stata Statistical Software, release 15.1; Stata Corp., College Station, TX, USA).
Statistical analyses were performed using SE STATA (Stata Statistical Software, release 15.1; Stata Corp., College Station, TX, USA).
8
Propensity Score Matching and Survival Analysis
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The independent t-test and the Chi-square test were used to analyze baseline differences either in the pre-match or post-match cohorts. We used a standardized mean difference (SMD) to define the matching efficacy. An SMD of <0.20 was taken as successful propensity matching between the groups.23 (link) The relationships between clinical and pathological factors and long-term progression-free survival (PFS) and overall survival (OS) were assessed using univariate log-rank tests. Univariate and multivariate Cox regression analysis was applied to identify the prognostic factors of OS and PFS. Tumor or treatment characteristics that achieved a p value <0.10 in univariate analysis were included in the multivariate analysis. For all analyses, p < 0.05 was considered statistically significant. Testing for trends can be applied based on various statistical hypotheses when necessary. Statistical analyses were performed using SE STATA (Stata Statistical Software, release 15.1; Stata Corp LLC, College Station, TX, USA).
9
Nonparametric Statistical Analyses
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Analyses were performed in Graphpad Prism (V7, Graphpad Software Inc.) and StataSE (StataCorp). As group sizes consisted of <12, two-sided, unpaired t tests using nonparametric statistics, Mann-Whitney U tests were applied using an α of 0.05 for all analyses involving independent samples and Wilcoxon signed-rank test for dependent samples. Data were presented as individual points with the calculated group mean (line) or as bar graphs with ± SEM error bars for each group.
10
Codesign Process for Texting Use Cases
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All the interviews and discussions across the 4 phases were conducted by at least 2 members of the codesign team (1 leading the discussion and the other recording notes). In total, 2 independent researchers, with master’s degrees, transcribed and open-coded the text separately and then openly compared the themes to reach a consensus on the final coding themes [57 ]. The emerging themes guided the content of the texting use cases and the foundational blocks for the several prototypes built and tested during the third and fourth phase (see Table 1 ).
The data collected during the pilot studies and the self-administered Web-based staff questionnaire were deidentified, coded, and analyzed using STATA SE, a statistical software package used by social science researchers [58 ]. The information gathered through all 4 phases was ethically conducted and was approved by the Institutional Review Board at the University of Illinois, Chicago.
The data collected during the pilot studies and the self-administered Web-based staff questionnaire were deidentified, coded, and analyzed using STATA SE, a statistical software package used by social science researchers [58 ]. The information gathered through all 4 phases was ethically conducted and was approved by the Institutional Review Board at the University of Illinois, Chicago.
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