Multivariable regression: understanding one of medicine’s most fundamental statistical tools

Nathan H. Varady; Ayoosh Pareek; Christina M. Eckhardt; Riley J. Williams; Sophia J. Madjarova; Matthieu Ollivier; R. Kyle Martin; Jón Karlsson; Benedict U. Nwachukwu

doi:10.1007/s00167-022-07215-9

Multivariable regression: understanding one of medicine’s most fundamental statistical tools

Nathan H. Varady, Ayoosh Pareek, Christina M. Eckhardt, Riley J. Williams, Sophia J. Madjarova, Matthieu Ollivier, R. Kyle Martin, Jón Karlsson, Benedict U. Nwachukwu

Orthopaedic Surgery

Research output: Contribution to journal › Review article › peer-review

8 Scopus citations

Abstract

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

Original language	English (US)
Pages (from-to)	7-11
Number of pages	5
Journal	Knee Surgery, Sports Traumatology, Arthroscopy
Volume	31
Issue number	1
DOIs	https://doi.org/10.1007/s00167-022-07215-9
State	Published - Jan 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).

Keywords

Multivariable regression
Predictive modeling
Statistics

PubMed: MeSH publication types

Journal Article
Review

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10.1007/s00167-022-07215-9

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title = "Multivariable regression: understanding one of medicine{\textquoteright}s most fundamental statistical tools",

abstract = "Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.",

keywords = "Multivariable regression, Predictive modeling, Statistics",

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note = "Publisher Copyright: {\textcopyright} 2022, The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).",

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doi = "10.1007/s00167-022-07215-9",

language = "English (US)",

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T2 - understanding one of medicine’s most fundamental statistical tools

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AU - Pareek, Ayoosh

AU - Eckhardt, Christina M.

AU - Williams, Riley J.

AU - Madjarova, Sophia J.

AU - Ollivier, Matthieu

AU - Martin, R. Kyle

AU - Karlsson, Jón

AU - Nwachukwu, Benedict U.

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N2 - Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

AB - Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

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KW - Predictive modeling

KW - Statistics

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Multivariable regression: understanding one of medicine’s most fundamental statistical tools

Abstract

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Keywords

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