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Cited by 1,291Statistical Methods in EpidemiologyAdvanced Statistical Methods and ModelsStatistical Methods and Applications

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

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P. Vatcheva, Kristina, Lee, MinJae·2016·Epidemiology: Open Access
DOI 10.4172/2161-1165.1000227PMC PMC4888898
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Abstract

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

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  • All variance inflation factors were below 5.0, with mean VIF of 1.25, indicating acceptable levels of multicollinearity in the multivariable model.

    “All variance inflation factors (VIFs) were below the threshold of 5.0, with mean VIF of 1.25, indicating acceptable levels of multicollinearity in our multivariable model (Table S1) [42].”

    From Factors associated with postpartum depression symptoms following antepartum hospitalization (2026) · cites this paper as [42]

    The Discussion (Multicollinearity diagnostic) identifies VIF greater than 5 as a suggested cutoff, supporting the citing paper’s reference to that threshold. But the Conclusions explicitly warn that VIF below 5 does not always indicate low multicollinearity, so the cited paper does not support treating the reported VIFs alone as proof of acceptable levels. The mean VIF of 1.25 is presented as a result from the citing authors’ own model, not as a result attributed to this source. (LLM, conf=0.85)

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Research works printed but not linked: 39

  1. [1]Aiken LS West SG 1991 Multiple regression: Testing and interpreting interactions Newbury Park C editor SAGE Publications, Inc
  2. [2]Gordon RA 1968 Issues in Multiple Regression The American Journal of Sociology 73 592 616
  3. [4]Mason G 1987 Coping with multicollinearity The Canadian Journal of program evaluation 2 87 93
  4. [5]Mason Charlotte H Perreault William D Jr 1991 Collinearity, Power, and Interpretation of Multiple Regression Analysis Journal of Marketing Research 28 268 280
  5. [7]Tu YK Clerehugh V Gilthorpe MS 2004 Collinearity in linear regression is a serious problem in oral health research Eur J Oral Sci 112 389 397 15458496
  6. [9]Erdeniz B Rohe T Done J Seidler RD 2013 A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes Front Neurosci 7 Article 116 23882174 PMC3715737
  7. [10]Dormann CF Elith J Bacher S 2013 Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Echography 36 27 46
  8. [11]Farrar DE Glauber RR 1967 Multicollinearity in regression analysis: the problem revisited Review of Economics and Statistics 49 92 107
  9. [13]Hoffmann JP Shafer K 2015 Linear Regression Analysis: Applications and Assumptions 2nd NASW Press Washington, DC
  10. [14]Kutner M Nachtsheim C Neter J 2004 Applied Linear Statistical Models 4th McGraw-Hill Irwin
  11. [15]Tamimi RM Hankinson SE Campos H Spiegelman D Zhang S 2005 Plasma carotenoids, retinol, and tocopherols and risk of breast cancer Am J Epidemiol 161 153 160 15632265
  12. [16]Leal C Bean K Thomas F Chaix B 2012 Multicollinearity in associations between multiple environmental features and body weight and abdominal fat: using matching techniques to assess whether the associations are separable Am J Epidemiol 175 1152 62 22534206
  13. [17]Fleten C Nystad W Stigum H Skjaerven R Lawlor DA 2012 Parent-offspring body mass index associations in the Norwegian Mother and Child Cohort Study: a family-based approach to studying the role of the intrauterine environment in childhood adiposity Am J Epidemiol 176 83 92 22771730 PMC3493198
  14. [18]Rotimi C Cooper R 1995 Familial resemblance for anthropometric measurements and relative fat distribution among African Americans Int J Obes Relat Metab Disord 19 875 880 8963355
  15. [19]Pyke SD Wood DA Kinmonth AL Thompson SG 1997 Change in coronary risk and coronary risk factor levels in couples following lifestyle intervention. The British Family Heart Study Arch Fam Med 6 354 360 9225707
  16. [20]Inoue K Sawada T Suge H Nao Y Igarashi M 1996 Spouse concordance of obesity, blood pressures and serum risk factors for atherosclerosis J Hum Hypertens 10 455 459 8880559
  17. [21]Jeffery RW Rick AM 2002 Cross-sectional and longitudinal associations between body mass index and marriage-related factors Obes Res 10 809 815 12181390
  18. [23]Desai CS Colangelo LA Liu K Jacobs DR Cook 2013 Prevalence, prospective risk markers, and prognosis associated with the presence of left ventricular diastolic dysfunction in young adults: the coronary artery risk development in young adults study Am J Epidemiol 177 20 32 23211639 PMC3590041
  19. [24]Mungreiphy NK Kapoor S Sinha R 2011 Association between BMI, Blood Pressure, and Age: Study among Tangkhul Naga Tribal Males of Northeast India Journal of Anthropology Article ID 748147 6
  20. [25]Dagan SS Segev S Novikov Dankner R 2013 Waist circumference vs body mass index in association with cardiorespiratory fitness in healthy men and women: a cross sectional analysis of 403 subjects Nutrition Journal 12 PMC3564926 23317009
  21. [26]Feller S Boeing H Pischon T 2010 Body mass index, waist circumference, and the risk of type 2 diabetes mellitus: implications for routine clinical practice Dtsch Arztebl Int 107 470 476 20644701 PMC2905837
  22. [27]Janssen I Katzmarzyk PT Ross R 2004 Waist circumference and not body mass index explains obesity-related health risk. below Am J Clin Nutr 79 379 384 14985210
  23. [28]Peters TJ 2008 Multifarious terminology: multivariable or multivariate? univariable or univariate? Pediatric Perinat Epidemiol 22 506 19000286
  24. [29]Fisher-Hoch SP Rentfro AR Salinas JJ Perez A Brown HS 2010 Socioeconomic status and prevalence of obesity and diabetes in a Mexican American community, Cameron County, Texas, 2004–2007 Prev Chronic Dis 7 A53 20394692 PMC2879985
  25. [31]Kleinbaum DG Kupper LL Nizam A Muller KE 2008 Applied Regression Analysis and Other Multivariable Methods 4th Duxbury Press
  26. [32]Guimarães IC de Almeida AM Santos AS Barbosa DB Guimarães AC 2008 Blood pressure: effect of body mass index and of waist circumference on adolescents Arq Bras Cardiol 90 393 399 18592092
  27. [33]Al-Sendi AM Shetty P Musaiger AO Myatt M 2003 Relationship between body composition and blood pressure in Bahraini adolescents Br J Nutr 90 837 844 14552329
  28. [34]Lara M Bustos P Amigo H Silva C Rona RJ 2012 Is waist circumference a better predictor of blood pressure, insulin resistance and blood lipids than body mass index in young Chilean adults? BMC Public Health 12 638 22882972 PMC3441472
  29. [35]Song YH 2014 The association of blood pressure with body mass index and waist circumference in normal weight and overweight adolescents Korean J Pediatr 57 79 84 24678332 PMC3965799
  30. [36]Donath C Grässel E Baier D Pfeiffer C Bleich S 2012 Predictors of binge drinking in adolescents: ultimate and distal factors – a representative study BMC Public Health 12 263 22469235 PMC3378431
  31. [37]Berry WD Feldman S 1985 Multiple Regression in Practice (Quantitative Applications in the Social Sciences) SAGE Publications Thousand Oaks. CA
  32. [39]Belsley DA 1991 Conditioning Diagnostics: Collinearity and Weak Data in Regression 1st John Wiley & Sons
  33. [40]Freund RJ Wilson WJ 1998 Regression Analysis: Statistical Modeling of a Response Variable 1st Academic Press
  34. [41]Belsley DA Kuh E Welsch RE 1980 Regression Diagnostics: Identifying Influential Data and Sources of Collinearity John Wiley & Sons New York
  35. [42]Pearson K 1901 On Lines and Planes of Closest Fit to Systems of Points in Space Philosophical Magazine 2 559 572
  36. [43]Hotelling H 1933 Analysis of a complex of statistical variables into principal components Journal of Educational Psychology 24 417 441 498 520
  37. [46]Johnson RA Wichern DW 2007 Applied multivariate statistical analysis 6th Pearson Prentice Hall London
  38. [47]Haase RF 2011 Multivariate General Linear Models SAGE Publications Thousand Oaks, CA
  39. [48]Rahbar MH Samms-Vaughan M Loveland KA Pearson DA Bressler J 2012 Maternal and paternal age are jointly associated with childhood autism in Jamaica J Autism Dev Disord 42 1928 1938 22230961 PMC3858006

Cited by

Sorted by citations

Authors
P. Vatcheva, K., & Lee, M.
Affiliations
The University of Texas Health Science Center at Houston (US)
ORCID
Lee, MinJae
Venue
Epidemiology: Open Access
Publisher
OMICS Publishing Group
Published
2016
Volume
06
Issue
02
ISSN
2161-1165 (ISSN-L)
DOI
10.4172/2161-1165.1000227
PMC
PMC4888898
OpenAlex
W2371547131
Cited by
1,291
References
1
Citations by year
2026: 147 · 2025: 215 · 2024: 195 · 2023: 155 · 2022: 163 · 2021: 155 · 2020: 117 · 2019: 78 · 2018: 42 · 2017: 17 · 2016: 2 · 2014: 1
FWCI
46.94
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Epidemiology Open Access (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion, cc-by)
Funders
  • National Institutes of Health (UL1TR000371, TR000371)
  • Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston (UL1TR000371, TR000371)
  • National Center for Advancing Translational Sciences (TR000371)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
multicollinearity, regression analysis, epidemiological studies, interpretation of regression results, Cameron County Hispanic Cohort, simulated data

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