Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology.
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 mult...
| Published in: | Quality of Life Research Vol. 28; no. 5; pp. 1315 - 1326 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
May2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135927729&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135927729 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09629343 GPQ jtl: Quality of Life Research issn: 09629343 maglogo: N pubinfo: dt: May2019 vid: 28 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135927729 135927729 NLM30659449 10.1007/s11136-018-02097-2 NLM30659449 135927729 ppf: 1315 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology. aug: au: Cottone, Francesco Deliu, Nina Collins, Gary S. Anota, Amelie Bonnetain, Franck Van Steen, Kristel Cella, David Efficace, Fabio affil: Data Center and Health Outcomes Research Unit, Italian Group for Adult Hematologic Diseases (GIMEMA), Rome, Italy sug: subj: Quality of Life Psychosocial Factors Neoplasms Psychosocial Factors Neoplasms Therapy Young Adult Middle Age Aged, 80 and Over Male Adult Factor Analysis Prognosis Cox Proportional Hazards Model Female Aged Sample Size Impact of Events Scale Middle Aged: 45-64 years Aged, 80 & over Adult: 19-44 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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