Distinction Between Two Statistical Terms: Multivariable and Multivariate Logistic Regression

Kalan, Mohammad Ebrahimi; Jebai, Rime; Zarafshan, Elaheh; Bursac, Zoran

doi:10.1093/ntr/ntaa055

Cited by 15 publications

(10 citation statements)

References 10 publications

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“…Additionally, In order to estimate the unique relationship between the included variables and CKD status multivariable logistic regression was performed. Multivariable logistic analysis (MVLA) model is selected because it is e cient method for the analysis of with one outcome (dependent) and multiple independent variables [41]. In this study CKD status is taken as dependent variable while other factors are taken as independent variable.In statistical analysis to identify the signi cance of the independent variable P value is the taken as a measurement tool.…”

Section: Discussionmentioning

confidence: 99%

Development of Chronic Kidney Disease Risk Prediction and Management System- Research study

Asmare

Thamineni

Dasari

et al. 2023

Preprint

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Background: Chronic kidney disease is one of a major global public health issue, affecting over 10% of the population worldwide. It is the leading cause of death in 2016 ranking 16th and is expected to rise to 5th rank by 2040.Consequently, tools to identify patients at high risk of having CKD and management of risk factors are needed, particularly in limited-resources settings where laboratory facilities are scarce. This study aimed to develop a risk prediction and management system using data from JUMC, SPHMMC and MTUTH. Objective: To develop chronic kidney disease risk prediction and management system is using expert system. Method :General chronic kidney disease risk factor were collected from expert knowledge .The identified general risk factors were applied on 384 patients data collected from three hospitals to identify risk factors in Ethiopia .The risk factors were identified using statistical analysis .After identifying the risk factors from the statistical analysis ,risk factor managements techniques were identified from expert knowledge. Knowledge gained from the expert knowledge and statistical analyses were combined and developed using rule based expert system. Main outcome measure: Accuracy, Precision and recall are the parameters which have been evaluated from the developed system using confusion matrix. Result: The system has showed 63.3 %, 65.3 %and 77.5%accuracy at 14%, 24% and 34% cut off percent respectively in estimating probability. Conclusion: This study will have significance in preventing chronic kidney disease at early stage and creating awareness. Funding Statement: The authors received no specific funding for this study.

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Section: Discussionmentioning

confidence: 99%

Development of Chronic Kidney Disease Risk Prediction and Management System- Research study

Asmare

Thamineni

Dasari

et al. 2023

Preprint

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“…The terms 'multivariate' and 'multivariable' are often used: a multivariable model refers to an analysis with one dependent and two or more (multiple) independent variables whereas a multivariate analysis refers to an analysis with more than one outcomes (for example repeated measures) and multiple independent variables. 21 JAMA editors strongly suggest avoiding causal language except in randomized controlled trials (https://jamanetwork.com/ journals/jama/pages/instructions-for-authors#SecReportsofSurveyResearch). Furthermore, they also advise describing methods and results using the words 'association' and 'correlation' and to avoid avoiding words that imply a 'cause-and-effect' relationship.…”

Section: Area 1: Titles Of the Articles With Multiple Regression Analysis And The Use Of Causal Language In The Manuscriptsmentioning

confidence: 99%

The ABC of linear regression analysis: What every author and editor should know

Baždarić¹,

Šverko²,

Salarić³

et al. 2021

ESE

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Regression analysis is a widely used statistical technique to build a model from a set of data on two or more variables. Linear regression is based on linear correlation, and assumes that change in one variable is accompanied by a proportional change in another variable. Simple linear regression, or bivariate regression, is used for predicting the value of one variable from another variable (predictor); however, multiple linear regression, which enables us to analyse more than one predictor or variable, is more commonly used. This paper explains both simple and multiple linear regressions illustrated with an example of analysis and also discusses some common errors in presenting the results of regression, including inappropriate titles, causal language, inappropriate conclusions, and misinterpretation.

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“…Although often used interchangeably, the terms “ multivariable analysis ” and “ multivariate analysis ” are distinct. A multivariable model is an analysis with a single endpoint but multiple independent variables, whereas a multivariate analysis describes multiple study outcome endpoints, i.e., different adverse events with multiple independent variables [72] , [73] , [74] , using a single model and provides unbiased and precise parameter estimation and potentially increased statistical power.…”

Section: Addressing Confounding Variablesmentioning

confidence: 99%

Statistical fundamentals on cancer research for clinicians: Working with your statisticians

Huang

et al. 2021

Clinical and Translational Radiation Oncology

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To facilitate understanding statistical principles and methods for clinicians involved in cancer research. Methods: An overview of study design is provided on cancer research for both observational and clinical trials addressing study objectives and endpoints, superiority tests, non-inferiority and equivalence design, and sample size calculation. The principles of statistical models and tests including contemporary standard methods of analysis and evaluation are discussed. Finally, some statistical pitfalls frequently evident in clinical and translational studies in cancer are discussed. Results: We emphasize the practical aspects of study design (superiority vs non-inferiority vs equivalence study) and assumptions underpinning power calculations and sample size estimation. The differences between relative risk, odds ratio, and hazard ratio, understanding outcome endpoints, purposes of interim analysis, and statistical modeling to minimize confounding effects and bias are also discussed. Conclusion: Proper design and correctly constructed statistical models are critical for the success of cancer research studies. Most statistical inaccuracies can be minimized by following essential statistical principles and guidelines to improve quality in research studies.

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Distinction Between Two Statistical Terms: Multivariable and Multivariate Logistic Regression

Cited by 15 publications

References 10 publications

Development of Chronic Kidney Disease Risk Prediction and Management System- Research study

Development of Chronic Kidney Disease Risk Prediction and Management System- Research study

The ABC of linear regression analysis: What every author and editor should know

Statistical fundamentals on cancer research for clinicians: Working with your statisticians

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