Pooled HR and 95% CI were used as common measurements for assessing the strength between pretreatment PLR and survival of CRC. Cochrane Q test and Higgins I‐squared statistics were performed to assess the heterogeneity of pooled studies. I‐square > 50% and PH < 0.1 were considered as a measure of substantial heterogeneity among studies, then random‐effects model (DerSimonian–Laird method) 30 was used to calculate the pooled HR. Otherwise, fixed effects model (Mantel–Haenszel method) 31 was performed. Subgroup analysis was conducted to explore the sources of heterogeneity. Publication bias was assessed by Begg's funnel plot and Egger's linear regression test 32 . The sensitivity analysis was performed to estimate the stability of outcome. All analyses were carried out by stata 11.0 statistical software (STATA Corporation, College Station, TX, USA) and P < 0.05 was considered statistically significant.
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Lab products found in correlation
39 protocols using stata 11.0 statistical software
1
Meta-analysis of PLR and CRC Survival
2
Comparing NIV Withdrawal Strategies
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In the absence of any study comparing three strategies of withdrawal of NIV, we computed sample size to detect a minimum absolute difference of 40% (30% vs. 70%) between any two groups, with a confidence level of 95% and power of 80%. With this assumption, a sample size of 29 evaluable patients in each of the three groups was calculated and decided to enroll 30 patients in each group.
Variables following approximately normal distribution were summarized by mean ± standard deviation, and one-way analysis of variance was used to compare the three groups. For the analysis of primary and secondary objectives, the effect size (difference in percentage of patients not requiring reinstitution of NIV) and its 95% confidence interval were computed. The total duration of NIV use and hospital stay was calculated from the 1st day of NIV initiation and admission in the emergency, ward, or ICU, respectively. For the secondary objective (time to recurrence of HcRF), the analysis was done using the Kaplan–Meier survival analysis. All analysis was performed as per the principles of intention to treat analysis. STATA 11.0 statistical software (StataCorp. LP, Texas, USA) was used for the analysis. All statistical tests were two-tailed and P ≤ 0.05 was considered statistically significant.
Variables following approximately normal distribution were summarized by mean ± standard deviation, and one-way analysis of variance was used to compare the three groups. For the analysis of primary and secondary objectives, the effect size (difference in percentage of patients not requiring reinstitution of NIV) and its 95% confidence interval were computed. The total duration of NIV use and hospital stay was calculated from the 1st day of NIV initiation and admission in the emergency, ward, or ICU, respectively. For the secondary objective (time to recurrence of HcRF), the analysis was done using the Kaplan–Meier survival analysis. All analysis was performed as per the principles of intention to treat analysis. STATA 11.0 statistical software (StataCorp. LP, Texas, USA) was used for the analysis. All statistical tests were two-tailed and P ≤ 0.05 was considered statistically significant.
3
Predicting Survival Outcomes Using NPAR
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Using a receiver operating characteristic curve, we determined that a NPAR cut-off of 18 provided the optimal balance between sensitivity and specificity in our cohort, so patients were classified into a low and high NPAR group (NPAR ≤18 or NPAR >18). Association of NPAR with categorical variables was assessed using χ2 tests; differences in continuous variables were analyzed using Mann–Whitney U-test. Kaplan–Meier method was used to estimate disease-free survival, OS and CSS; log-rank tests were applied for pair wise comparison of survival. Univariable and multivariable Cox regression models addressed associations with OS and CSS adjusting for the effects of standard clinical and pathological features. Harrell’s concordance index (C index) was used to measure the ordinal predictive power of the model for disease-free survival (data not shown), OS and CSS. All p-values were two sided, and statistical significance was defined as p < 0.05. Statistical analyses were performed using Stata 11.0 statistical software (Stata Corp., TX, USA).
4
Data Pooling and Meta-Analysis Protocol
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STATA11.0 statistical software (http://www.stata.com ) was used for data pooling. The treatment response was demonstrated by objective response rate (ORR) and calculated by the equation of ORR = complete response (CR) + partial response (PR). The ORR and treatment associated toxicity was expressed by odds ratio (OR) and corresponding 95% confidence interval (95% CI). The statistical heterogeneity across the included 18 studies was investigated by I2 test. Publication bias was assessed by Begg's funnel plot and Egger's line regression test (Figure 1 ).
5
Statistical Analysis of Measurement Data
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The statistical analysis was performed using STATA11.0 statistical software ( http://www.stata.com ) , the measurement data were expressed with x ¯ ± s
6
Factors Associated with Blood Abnormalities in Heart Failure
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The data was entered into Epi-Data version 4.6.0.2 software and then exported to Stata 11.0 statistical software (Stata Corp, College Station, TX, USA) for analysis. To summarize the characteristics of the study participant’s descriptive statistics were performed. Factors associated with platelet, neutrophil and lymphocyte abnormalities in heart failure patients were assessed by performing binary logistic regression analyses. Crude odds ratio (COR) and adjusted odds ratio (AOR) with a 95% confidence interval (CI) were calculated to determine the strength of the association of independent variables with the outcome variables. A p-value of <0.05 was considered statistically significant.
7
Statistical Analysis of TBI-Associated Coagulopathy
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Statistical analysis was performed for the comparison of parameters for two outcomes: firstly, TBI-associated coagulopathy and no TBI-associated coagulopathy; and secondly, survivors and non-survivors during the hospital stay. Summary statistics were used to describe continuous variables as mean ± standard deviation or median [interquartile range (IQR)] and categorical data were presented as frequency (%). Analysis was performed using STATA 11.0 statistical software (StataCorp LLC, College Station, TX, USA). Univariate analysis of the continuous variables between the coagulopathic and non-coagulopathic study group was assessed using t-tests/Wilcoxon rank-sum. Chi χ2 tests or Fisher’s exact test was used to compare categorical variables. Statistical significance was set at the p < 0.05 level. The correlation between continuous variables was explored with Spearman’s rank correlation coefficient. Receiver operating characteristic (ROC) curve analysis was used to generate optimal cut-offs for endothelial damage markers found to be significant in univariate analysis for identification of isolated sTBI patients with early coagulopathy.
8
Postoperative Complications: Statistical Analysis
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Associations of postoperative complications with categorical variables were assessed using the chi-square tests, while differences in means of continuous variables were analyzed using the t test. Logistic regression analysis was performed to assess the association of predictive factors with complications. Sub-group analyses according to clamping technique were also done. All P values were 2-sided, and statistical significance was defined as a P <.05. Statistical analyses were performed using Stata 11.0 statistical software (Stata Corp., College Station, TX).
9
Patient Preferences and Experiences in ICU Vs. Floor
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We conducted data formatting and recoding of variables using STATA 11.0 statistical software (STATA Corp, College Station, TX). The study population was described using descriptive statistics. We reported normal data with means and standard deviations and proportions were presented with 95% CIs.
For the open-ended questionnaire, transcriptions were uploaded into ATLAS.ti (Belin, Germany), a qualitative data analysis software program. Transcripts were reviewed by two authors (DN and JM) who independently generated an exhaustive list of items representing emergent themes and factors regarding patient preferences, perceived differences between the ICU and the floor, and hospitalization factors important to patients. This exhaustive list was narrowed to generate a summative list of themes and factors. We developed coding criteria and systematically applied them to the formatted transcripts by “tagging” elements within the transcripts. “Tagged” elements were quantitatively assessed to identify predominant factors and common themes. We then chose from the transcripts specific quotes that best represented these factors and themes.
For the open-ended questionnaire, transcriptions were uploaded into ATLAS.ti (Belin, Germany), a qualitative data analysis software program. Transcripts were reviewed by two authors (DN and JM) who independently generated an exhaustive list of items representing emergent themes and factors regarding patient preferences, perceived differences between the ICU and the floor, and hospitalization factors important to patients. This exhaustive list was narrowed to generate a summative list of themes and factors. We developed coding criteria and systematically applied them to the formatted transcripts by “tagging” elements within the transcripts. “Tagged” elements were quantitatively assessed to identify predominant factors and common themes. We then chose from the transcripts specific quotes that best represented these factors and themes.
10
Hepatic Steatosis Impact on Surgical Outcomes
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Two-way analysis was used to compare preoperative characteristics, intraoperative details, and postoperative outcomes between the subgroups of patients with (study group) or without (control group) NAC-associated hepatic steatosis. Chi-square test was used for categorical variables, and Student’s T test was used for continuous variables. Multivariable logistic regression models were used to evaluate the effect of hepatic steatosis on 30-day mortality, overall morbidity, and major morbidity while adjusting for other confounders. A p value of less than 0.05 was considered significant. All analyses were conducted with Stata 11.0 statistical software (College Station, TX).
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