2021
DOI: 10.1016/j.jhsa.2020.10.024
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The Problem of Collinearity in Mental Health and Patient Reported Outcome Research

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Cited by 9 publications
(5 citation statements)
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“…We think this is unlikely because we used short forms and computer adaptive tests to make the total time to complete the questionnaire less than 10 minutes. Although none of the variables met the traditional criteria for multicollinearity, we were thoughtful about the potential for multiple variables to attenuate associations in multivariable models [37]. Even though this merits additional study, the likely result of a better strategy for potential collinearity would likely strengthen the observed associations, which bolsters our confidence in the findings.…”
Section: Discussionmentioning
confidence: 96%
“…We think this is unlikely because we used short forms and computer adaptive tests to make the total time to complete the questionnaire less than 10 minutes. Although none of the variables met the traditional criteria for multicollinearity, we were thoughtful about the potential for multiple variables to attenuate associations in multivariable models [37]. Even though this merits additional study, the likely result of a better strategy for potential collinearity would likely strengthen the observed associations, which bolsters our confidence in the findings.…”
Section: Discussionmentioning
confidence: 96%
“…One benefit of the OSPRO-YF is that in addition to score estimates for each of the 11 full-length psychologic questionnaires, it indicates which of the score estimates is in the highest quartile of population scores for those questionnaires. This function flags high scores, essentially notifying busy clinicians which psychologic constructs (including measures of coping) are of the highest concern [52].…”
Section: Discussionmentioning
confidence: 99%
“…For instance, if clinicians can understand which constructs are most strongly associated with capability, and therefore warrant particular attention, they can prioritize their approach to assessment and intervention, accounting for mindset. Statistically, methods such as factor analysis can group mental health characteristics into factors that can minimize redundancy, be incorporated in multivariable regression analyses, and address multicollinearity, where highly correlated variables entered into the same model may lead to false-negative results [23,52].…”
Section: Introductionmentioning
confidence: 99%
“…Due to multicollinearity (intercorrelation) between the mental health variables (Appendix 1), we decided to run multiple models with one mental health variable at the time and to only include the model that had the best model fit (lowest Akaike Information Criterion). 28 The model with NPTQ-4 had the best fit and was selected, and other mental health variables were excluded. ACEs were not moved to multivariable analysis since the P value was above the .10 threshold in bivariate analysis.…”
Section: Methodsmentioning
confidence: 99%