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

  • Francesco CottoneEmail author
  • Nina Deliu
  • Gary S. Collins
  • Amelie Anota
  • Franck Bonnetain
  • Kristel Van Steen
  • David Cella
  • Fabio Efficace
Article

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.

Keywords

Health-related quality of life Multicollinearity Patient-reported outcomes Prognostic factor analysis Ridge regression 

Notes

Author contributions

FC, FE: Conception and design, FC, ND, FE: Statistical analyses, all authors: Interpretation of results, all authors: Manuscript writing.

Compliance with ethical standards

Conflict of interest

No potential conflict of interest for this paper was reported by the authors.

Supplementary material

11136_2018_2097_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (PDF 1541 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Center and Health Outcomes Research UnitItalian Group for Adult Hematologic Diseases (GIMEMA)RomeItaly
  2. 2.Centre for Statistics in MedicineNDORMS, University of OxfordOxfordUK
  3. 3.Methodology and Quality of Life in Oncology Unit (INSERM UMR 1098)University Hospital of BesançonBesançonFrance
  4. 4.French National Platform Quality of Life and CancerBesançonFrance
  5. 5.GIGA-R Medical Genomics UnitUniversity of LiègeLiègeBelgium
  6. 6.Department of Human Genetics – Systems MedicineUniversity of LeuvenLeuvenBelgium
  7. 7.Department of Medical Social Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoUSA

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