In the clinical researches, large number of clinical verifications has demonstrated that the Brain Natriuretic Peptide can be used in heart failure detection. Some relevant studies illustrate Plasma Brain Natriuretic Peptide can be affected by many factors, such as gender, age, environment of therapy, and so forth. This paper analyzes valid data of a clinical experiment, and finds out the influence of concomitant variables in diagnose of heart failure, then analyzes the outcome of rhNRG-1 on each individual. The phrase ‘Brain Natriuretic Peptide’ in the article specified N-terminal Prohormone of Brain Natriuretic Peptide (Nt-Pro BNP) in this dissertation. In this paper, the main analyzing method is Logistic Regression. It is used for estimating the Parameters of a qualitative model. The outcome, in other words, the probabilities is to describe the possible results of a single trial. By using this method, we could discuss the triggers of diseases and popularize it to other problems that concentrate on the cause. Moreover, the binary logistic model is for predicting a binary response based on one or more predictor variables. The main steps of this dissertation is data screening, missing values handling, descriptive statistics analyzing, then the Logistic Regression, and finally draw a conclusion.
| Published in | American Journal of Clinical and Experimental Medicine (Volume 3, Issue 5) |
| DOI | 10.11648/j.ajcem.20150305.14 |
| Page(s) | 222-227 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
N-terminal Prohormone, Brain Natriuretic Peptide, Heart Failure, Biostatistics, Logistic Regression
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APA Style
Yizeng Li, Hong Zhang. (2015). Statistical Analysis of Brain Natriuretic Peptide in the Treatment of Heart Failure. American Journal of Clinical and Experimental Medicine, 3(5), 222-227. https://doi.org/10.11648/j.ajcem.20150305.14
ACS Style
Yizeng Li; Hong Zhang. Statistical Analysis of Brain Natriuretic Peptide in the Treatment of Heart Failure. Am. J. Clin. Exp. Med. 2015, 3(5), 222-227. doi: 10.11648/j.ajcem.20150305.14
AMA Style
Yizeng Li, Hong Zhang. Statistical Analysis of Brain Natriuretic Peptide in the Treatment of Heart Failure. Am J Clin Exp Med. 2015;3(5):222-227. doi: 10.11648/j.ajcem.20150305.14
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Download@article{10.11648/j.ajcem.20150305.14,
author = {Yizeng Li and Hong Zhang},
title = {Statistical Analysis of Brain Natriuretic Peptide in the Treatment of Heart Failure},
journal = {American Journal of Clinical and Experimental Medicine},
volume = {3},
number = {5},
pages = {222-227},
doi = {10.11648/j.ajcem.20150305.14},
url = {https://doi.org/10.11648/j.ajcem.20150305.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20150305.14},
abstract = {In the clinical researches, large number of clinical verifications has demonstrated that the Brain Natriuretic Peptide can be used in heart failure detection. Some relevant studies illustrate Plasma Brain Natriuretic Peptide can be affected by many factors, such as gender, age, environment of therapy, and so forth. This paper analyzes valid data of a clinical experiment, and finds out the influence of concomitant variables in diagnose of heart failure, then analyzes the outcome of rhNRG-1 on each individual. The phrase ‘Brain Natriuretic Peptide’ in the article specified N-terminal Prohormone of Brain Natriuretic Peptide (Nt-Pro BNP) in this dissertation. In this paper, the main analyzing method is Logistic Regression. It is used for estimating the Parameters of a qualitative model. The outcome, in other words, the probabilities is to describe the possible results of a single trial. By using this method, we could discuss the triggers of diseases and popularize it to other problems that concentrate on the cause. Moreover, the binary logistic model is for predicting a binary response based on one or more predictor variables. The main steps of this dissertation is data screening, missing values handling, descriptive statistics analyzing, then the Logistic Regression, and finally draw a conclusion.},
year = {2015}
}
TY - JOUR T1 - Statistical Analysis of Brain Natriuretic Peptide in the Treatment of Heart Failure AU - Yizeng Li AU - Hong Zhang Y1 - 2015/09/18 PY - 2015 N1 - https://doi.org/10.11648/j.ajcem.20150305.14 DO - 10.11648/j.ajcem.20150305.14 T2 - American Journal of Clinical and Experimental Medicine JF - American Journal of Clinical and Experimental Medicine JO - American Journal of Clinical and Experimental Medicine SP - 222 EP - 227 PB - Science Publishing Group SN - 2330-8133 UR - https://doi.org/10.11648/j.ajcem.20150305.14 AB - In the clinical researches, large number of clinical verifications has demonstrated that the Brain Natriuretic Peptide can be used in heart failure detection. Some relevant studies illustrate Plasma Brain Natriuretic Peptide can be affected by many factors, such as gender, age, environment of therapy, and so forth. This paper analyzes valid data of a clinical experiment, and finds out the influence of concomitant variables in diagnose of heart failure, then analyzes the outcome of rhNRG-1 on each individual. The phrase ‘Brain Natriuretic Peptide’ in the article specified N-terminal Prohormone of Brain Natriuretic Peptide (Nt-Pro BNP) in this dissertation. In this paper, the main analyzing method is Logistic Regression. It is used for estimating the Parameters of a qualitative model. The outcome, in other words, the probabilities is to describe the possible results of a single trial. By using this method, we could discuss the triggers of diseases and popularize it to other problems that concentrate on the cause. Moreover, the binary logistic model is for predicting a binary response based on one or more predictor variables. The main steps of this dissertation is data screening, missing values handling, descriptive statistics analyzing, then the Logistic Regression, and finally draw a conclusion. VL - 3 IS - 5 ER -
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