Antoine Barbieri1,2, Amélie Anota3,4, Thierry Conroy3,5, Sophie Gourgou-Bourgade1, Beata Juzyna6, Franck Bonnetain3,4, Christian Lavergne2,7, Caroline Bascoul-Mollevi1. 1. Biometrics Unit-CTD INCa, Institut régional du Cancer Montpellier (ICM) - Val d'Aurelle, Montpellier, France (AB, SG-B, CB-M) 2. Institut Montpelliérain Alexander Grothendieck, University of Montpellier, Montpellier, France (AB, CL) 3. National Quality of Life in Oncology Platform, France (AA, TC, FB) 4. Methodological and Quality of Life Unit in Oncology (EA 3181), Besançon, France (AA, FB) 5. Institut de Cancérologie de Lorraine, Nancy, France (TC) 6. Unicancer R&D, Paris, France (BJ) 7. Université Paul-Valéry Montpellier 3, Montpellier, France (CL)
Abstract
INTRODUCTION: A new longitudinal statistical approach was compared to the classical methods currently used to analyze health-related quality-of-life (HRQoL) data. The comparison was made using data in patients with metastatic pancreatic cancer. METHODS:Three hundred forty-two patients from the PRODIGE4/ACCORD 11 study were randomly assigned to FOLFIRINOX versus gemcitabine regimens. HRQoL was evaluated using the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30. The classical analysis uses a linear mixed model (LMM), considering an HRQoL score as a good representation of the true value of the HRQoL, following EORTC recommendations. In contrast, built on the item response theory (IRT), our approach considered HRQoL as a latent variable directly estimated from the raw data. For polytomous items, we extended the partial credit model to a longitudinal analysis (longitudinal partial credit model [LPCM]), thereby modeling the latent trait as a function of time and other covariates. RESULTS: Both models gave the same conclusions on 11 of 15 HRQoL dimensions. HRQoL evolution was similar between the 2 treatment arms, except for the symptoms of pain. Indeed, regarding the LPCM, pain perception was significantly less important in the FOLFIRINOX arm than in the gemcitabine arm. For most of the scales, HRQoL changes over time, and no difference was found between treatments in terms of HRQoL. DISCUSSION: The use of LMM to study the HRQoL score does not seem appropriate. It is an easy-to-use model, but the basic statistical assumptions do not check. Our IRT model may be more complex but shows the same qualities and gives similar results. It has the additional advantage of being more precise and suitable because of its direct use of raw data.
RCT Entities:
INTRODUCTION: A new longitudinal statistical approach was compared to the classical methods currently used to analyze health-related quality-of-life (HRQoL) data. The comparison was made using data in patients with metastatic pancreatic cancer. METHODS: Three hundred forty-two patients from the PRODIGE4/ACCORD 11 study were randomly assigned to FOLFIRINOX versus gemcitabine regimens. HRQoL was evaluated using the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30. The classical analysis uses a linear mixed model (LMM), considering an HRQoL score as a good representation of the true value of the HRQoL, following EORTC recommendations. In contrast, built on the item response theory (IRT), our approach considered HRQoL as a latent variable directly estimated from the raw data. For polytomous items, we extended the partial credit model to a longitudinal analysis (longitudinal partial credit model [LPCM]), thereby modeling the latent trait as a function of time and other covariates. RESULTS: Both models gave the same conclusions on 11 of 15 HRQoL dimensions. HRQoL evolution was similar between the 2 treatment arms, except for the symptoms of pain. Indeed, regarding the LPCM, pain perception was significantly less important in the FOLFIRINOX arm than in the gemcitabine arm. For most of the scales, HRQoL changes over time, and no difference was found between treatments in terms of HRQoL. DISCUSSION: The use of LMM to study the HRQoL score does not seem appropriate. It is an easy-to-use model, but the basic statistical assumptions do not check. Our IRT model may be more complex but shows the same qualities and gives similar results. It has the additional advantage of being more precise and suitable because of its direct use of raw data.
Authors: Antoine Barbieri; Jean Peyhardi; Thierry Conroy; Sophie Gourgou; Christian Lavergne; Caroline Mollevi Journal: BMC Med Res Methodol Date: 2017-09-26 Impact factor: 4.615