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Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology.

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Title: Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology.
Authors: Cottone, Francesco (AUTHOR), Deliu, Nina (AUTHOR), Collins, Gary S. (AUTHOR), Anota, Amelie (AUTHOR), Bonnetain, Franck (AUTHOR), Van Steen, Kristel (AUTHOR), Cella, David (AUTHOR), Efficace, Fabio (AUTHOR)
Source: Quality of Life Research. May2019, Vol. 28 Issue 5, p1315-1325. 11p. 2 Charts, 3 Graphs.
Subjects: Factor analysis, Akaike information criterion, Multicollinearity, Estimates, Oncology
Abstract: Purpose: The inclusion of patient-reported outcome (PRO) questionnaires in prognostic factor analyses in oncology has substantially increased in recent years. We performed a simulation study to compare the performances of four different modeling strategies in estimating the prognostic impact of multiple collinear scales from PRO questionnaires.Methods: We generated multiple scenarios describing survival data with different sample sizes, event rates and degrees of multicollinearity among five PRO scales. We used the Cox proportional hazards (PH) model to estimate the hazard ratios (HR) using automatic selection procedures, which were based on either the likelihood ratio-test (Cox-PV) or the Akaike Information Criterion (Cox-AIC). We also used Cox PH models which included all variables and were either penalized using the Ridge regression (Cox-R) or were estimated as usual (Cox-Full). For each scenario, we simulated 1000 independent datasets and compared the average outcomes of all methods.Results: The Cox-R showed similar or better performances with respect to the other methods, particularly in scenarios with medium-high multicollinearity (ρ = 0.4 to ρ = 0.8) and small sample sizes (n = 100). Overall, the Cox-PV and Cox-AIC performed worse, for example they did not select one or more prognostic collinear PRO scales in some scenarios. Compared with the Cox-Full, the Cox-R provided HR estimates with similar bias patterns but smaller root-mean-squared errors, particularly in higher multicollinearity scenarios.Conclusions: Our findings suggest that the Cox-R is the best approach when performing prognostic factor analyses with multiple and collinear PRO scales, particularly in situations of high multicollinearity, small sample sizes and low event rates. [ABSTRACT FROM AUTHOR]
Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology.
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  Data: <searchLink fieldCode="AR" term="%22Cottone%2C+Francesco%22">Cottone, Francesco</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deliu%2C+Nina%22">Deliu, Nina</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Collins%2C+Gary+S%2E%22">Collins, Gary S.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Anota%2C+Amelie%22">Anota, Amelie</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bonnetain%2C+Franck%22">Bonnetain, Franck</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Van+Steen%2C+Kristel%22">Van Steen, Kristel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cella%2C+David%22">Cella, David</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Efficace%2C+Fabio%22">Efficace, Fabio</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Quality+of+Life+Research%22">Quality of Life Research</searchLink>. May2019, Vol. 28 Issue 5, p1315-1325. 11p. 2 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Akaike+information+criterion%22">Akaike information criterion</searchLink><br /><searchLink fieldCode="DE" term="%22Multicollinearity%22">Multicollinearity</searchLink><br /><searchLink fieldCode="DE" term="%22Estimates%22">Estimates</searchLink><br /><searchLink fieldCode="DE" term="%22Oncology%22">Oncology</searchLink>
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  Data: <bold>Purpose: </bold>The inclusion of patient-reported outcome (PRO) questionnaires in prognostic factor analyses in oncology has substantially increased in recent years. We performed a simulation study to compare the performances of four different modeling strategies in estimating the prognostic impact of multiple collinear scales from PRO questionnaires.<bold>Methods: </bold>We generated multiple scenarios describing survival data with different sample sizes, event rates and degrees of multicollinearity among five PRO scales. We used the Cox proportional hazards (PH) model to estimate the hazard ratios (HR) using automatic selection procedures, which were based on either the likelihood ratio-test (Cox-PV) or the Akaike Information Criterion (Cox-AIC). We also used Cox PH models which included all variables and were either penalized using the Ridge regression (Cox-R) or were estimated as usual (Cox-Full). For each scenario, we simulated 1000 independent datasets and compared the average outcomes of all methods.<bold>Results: </bold>The Cox-R showed similar or better performances with respect to the other methods, particularly in scenarios with medium-high multicollinearity (ρ = 0.4 to ρ = 0.8) and small sample sizes (n = 100). Overall, the Cox-PV and Cox-AIC performed worse, for example they did not select one or more prognostic collinear PRO scales in some scenarios. Compared with the Cox-Full, the Cox-R provided HR estimates with similar bias patterns but smaller root-mean-squared errors, particularly in higher multicollinearity scenarios.<bold>Conclusions: </bold>Our findings suggest that the Cox-R is the best approach when performing prognostic factor analyses with multiple and collinear PRO scales, particularly in situations of high multicollinearity, small sample sizes and low event rates. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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