SAS Enterprise Guide 6.1 for physicians: correlation analysis

Objective: to develop algorithm of correlation analysis of prospective non-randomized clinical trial AMIRICABG (ClinicalTrials.gov Identifier: NCT03050489) data using SAS Enterprise Guide 6.1. Materials and methods. Data collection was performed according prospective non-randomized clinical trial AMIRICABG (ClinicalTrials.gov Identifier: NCT03050489) in Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia between 20162019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Correlation analysis was performed with SAS Enterprise Guide 6.1. Results. There was developed algorithm of correlation analysis data of prospective non-randomized clinical trial AMIRICABG (ClinicalTrials.gov Identifier: NCT03050489). This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of correlation analysis could make easier and improve efficient data analysis with SAS Enterprise Guide 6.1.

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SAS Enterprise Guide 6.1 for physicians: getting started

Medical academic journal ◽

10.17816/maj19327-36 ◽

2019 ◽

Vol 19 (3) ◽

pp. 27-36 ◽

Cited By ~ 1

Author(s):

Nikolay S. Bunenkov ◽

Gulnara F. Bunenkova ◽

Sergey A. Beliy ◽

Vladimir V. Komok ◽

Oleg A. Grinenko ◽

...

Keyword(s):

Clinical Trial ◽

Data Analysis ◽

Randomized Clinical Trial ◽

Clinical Laboratory ◽

Instrumental Data ◽

Saint Petersburg ◽

Develop Algorithm ◽

Efficient Data ◽

Medical University ◽

Efficient Data Analysis

Objective. To develop algorithm of data analysis of prospective non-randomized clinical trial AMIRICABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1. Materials and methods. Data collection was performed according prospective non-randomized clinical trial AMIRICABG in Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia between 20162019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Statistical analysis was performed with SAS Enterprise Guide 6.1. Results. There was developed algorithm of data analysis of prospective non-randomized clinical trial AMIRICABG. This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of data analysis could make easier and improve efficient data analysis. SAS Enterprise Guide 6.1 allows fast and accurate process big data.

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Sas enterprise guide 6.1: independent groups comparison for nominal data

Medical academic journal ◽

10.17816/maj34127 ◽

2020 ◽

Vol 20 (3) ◽

pp. 89-98

Author(s):

N. S. Bunenkov ◽

G. F. Bunenkova ◽

V. V. Komok ◽

O. A. Grinenko ◽

A S. Nemkov

Keyword(s):

Myocardial Infarction ◽

Clinical Trial ◽

Data Analysis ◽

Randomized Clinical Trial ◽

Postoperative Bleeding ◽

Nominal Data ◽

Saint Petersburg ◽

Develop Algorithm ◽

Chi Squared ◽

Medical University

Objective:to develop algorithm of independent groups comparison for nominal data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1. Materials and methods.Data collection was performed according to prospective non-randomized clinical trial AMIRI CABG in Pavlov First St. Petersburg State Medical University, Saint Petersburg, Russia between 2016-2019years with 336 patients. Patients were allocated into three groups of treatment. There is database which include following information: gender, myocardial infarction, stroke and postoperative bleeding. Comparison for nominal data (gender and incidence of myocardial infarction, stroke and bleeding) were calculated with SAS Enterprise Guide6.1 software with Chi-squared test and exact Fisher test. Results.There was developed algorithm of two independent groups comparison for nominal data. Conclusion.Presented algorithm of data analysis allows to compare independent groups for nominal data.

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SAS ENTERPRISE GUIDE 6.1 FOR PHYSICIANS: MULTIPLE COMPARISONS.

Medical academic journal ◽

10.17816/maj17736 ◽

2020 ◽

Author(s):

Nikolai S. Bunenkov ◽

Gulnara F. Bunenkova ◽

Vladimir V. Komok ◽

Oleg A. Grinenko ◽

Alexander S. Nemkov

Keyword(s):

Clinical Trial ◽

Data Analysis ◽

Randomized Clinical Trial ◽

Clinical Laboratory ◽

Multiple Comparisons ◽

Wilcoxon Test ◽

Process Data ◽

Bonferroni Adjustment ◽

Efficient Data ◽

Medical University

Objective: to develop algorithm of multiple comparisons data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) using SAS Enterprise Guide 6.1. Materials and methods. Data collection was performed according prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489) in 1Pavlov First St. Petersburg State Medical University, Saint-Petersburg, Russia between 2016-2019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Multiple comparisons test was performed with SAS Enterprise Guide 6.1. Results. There was developed algorithm of multiple comparisons data of prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489). This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of data analysis could make easier and improve efficient data analysis. SAS Enterprise Guide 6.1 allows fast and accurate process data Key words: SAS Enterprise Guide 6.1, statistical analysis, clinical trials, multiple comparisons, Bonferroni adjustment, Kruskal-Wallis test, Wilcoxon test, Tukey test.

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SAS Enterprise Guide 6.1: baseline characteristics presentation

Medical academic journal ◽

10.17816/maj64682 ◽

2021 ◽

Vol 21 (1) ◽

pp. 59-64

Author(s):

Nikolay S. Bunenkov ◽

Vladimir V. Komok ◽

Nikita V. Grudinin ◽

Vasiliy A. Bobylkov ◽

Gulnara F. Bunenkova ◽

...

Keyword(s):

Clinical Trial ◽

Randomized Clinical Trial ◽

Clinical Laboratory ◽

Foreign Journal ◽

Nominal Data ◽

Saint Petersburg ◽

Develop Algorithm ◽

Chi Squared ◽

Baseline Characteristics ◽

Medical University

Objective: to develop algorithm of presentation of baseline characteristics of patients which enrolled in prospective non-randomized clinical trial AMIRI CABG (ClinicalTrials.gov Identifier: NCT03050489). Materials and methods. Data collection was performed according to prospective non-randomized clinical trial AMIRI CABG in Pavlov First St. Petersburg State Medical University, Saint Petersburg, Russia between 2016-2019 years with 336 patients. Patients were allocated into three groups of treatment. There is database with clinical, laboratory and instrumental data. Comparison for nominal data (gender and incidence of myocardial infarction and stroke) were calculated with SAS Enterprise Guide 6.1 software with Chi-squared test and exact Fisher test. Baseline characteristics were presented in table. Results. There was developed algorithm of baseline characteristics presentation in APA-table. Conclusion. There was developed algorithm of baseline characteristics presentation SAS Enterprise Guide 6.1 could be useful for manuscript preparing for Russian and foreign journal.

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Design and implementation of a new prospective randomized clinical trial using informations of subset analysis data from previous trials

Controlled Clinical Trials ◽

10.1016/0197-2456(91)90278-t ◽

1991 ◽

Vol 12 (5) ◽

pp. 697

Author(s):

Junichi Sakamoto ◽

Satoshi Teramukai ◽

Hiroshi Kojima ◽

Yasuo Ohashi ◽

Hiroaki Nakazato

Keyword(s):

Clinical Trial ◽

Randomized Clinical Trial ◽

Analysis Data ◽

Prospective Randomized Clinical Trial ◽

Design And Implementation ◽

Subset Analysis

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Evaluating of prognostic value of biomarker and upper reference limit calculating

Medical academic journal ◽

10.17816/maj26271 ◽

2020 ◽

Vol 20 (2) ◽

pp. 79-86

Author(s):

Nikolay S. Bunenkov ◽

Gulnara F. Bunenkova ◽

Vladimir V. Komok ◽

Oleg A. Grinenko ◽

Alexander S. Nemkov

Keyword(s):

Logistic Regression ◽

Data Analysis ◽

Prognostic Value ◽

Troponin I ◽

Clinical Laboratory ◽

Artery Bypass ◽

Youden Index ◽

Reference Limit ◽

Saint Petersburg ◽

Upper Reference Limit

Objective: to develop algorithm of assessment of prognostic value of biomarker (troponin I) for predicting death after coronary artery bypass grafting. Materials and methods. Data collection was performed according to prospective non-randomized clinical trial AMIRI CABG in Pavlov First Saint Petersburg State Medical University, Saint Petersburg, Russia between 20162019 years with 336 patients. There is database with clinical, laboratory and instrumental data. Statistics were calculated with SAS Enterprise Guide 6.1 software. Prognostic capability of biomarker for death were evaluated with logistic regression. Spline of relation between death and biomarker level were plotted using coefficient of logistic regression and intercept. Upper reference limit was calculated with Youden index. Results. There was developed algorithm to assess prognostic value of biomarker and its usefulness for clinical application and to define upper reference limit of biomarker. This algorithm could be useful for physicians and researchers for data analysis. Conclusion. Presented algorithm of data analysis allows to assess prognostic value of novel biomarker and its clinical usefulness.

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