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

Francesco Cottone*, Nina Deliu, Gary S. Collins, Amelie Anota, Franck Bonnetain, Kristel Van Steen, David Cella, Fabio Efficace

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1315-1325
Number of pages11
JournalQuality of Life Research
Volume28
Issue number5
DOIs
StatePublished - May 15 2019

Keywords

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

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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