Construction of quality of life change patterns: example in oncology in a phase III therapeutic trial (FFCD 0307)

Health and Quality of Life Outcomes

Research

Gillles Nuemi¹  Côme Lepage¹  Hervé Devilliers¹  Catherine Quantin²  Karine Le Malicot³  Rosine Guimbaud⁴ 

[1] Centre Hospitalier Universitaire de Dijon, Service de biostatistique et d’Informatique Médicale (DIM), BP 77908, 21079, Dijon, Cedex, France;FFCD, Inserm, U866, Université de Bourgogne, Dijon, France;Centre Hospitalier Universitaire de Dijon, Service de biostatistique et d’Informatique Médicale (DIM), BP 77908, 21079, Dijon, Cedex, France;INSERM, CIC 1432, Dijon, France; Dijon University Hospital, Clinical Investigation Center, clinical epidemiology/ clinical trials unit, Dijon, France;Inserm UMR 1181, Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), University Bourgogne Franche-Comté, F-21000, Dijon, France;FFCD, Inserm, U866, Université de Bourgogne, Dijon, France;Inserm, UMR 1037/CNRS-ERL 5294, Université Toulouse 3, Toulouse, France;

关键词: Quality of life;    Change patterns;    Multiple imputation;    Clustering;   

DOI  :  10.1186/s12955-015-0342-1

 received in 2015-04-08, accepted in 2015-09-10,  发布年份 2015

来源: Springer

【 摘 要 】

ObjectiveQuality of life data in cancerology are often difficult to summarize due to missing data and difficulty to analyze the pattern of evolution in different groups of patients. The aim of this work was to apply a new methodology to construct Quality of Life (QoL) change patterns within patients included in a clinical trial comparing to regimen of treatment in locally advanced eosogastric cancer.Materials and methodsIn this trial, QoL was assessed every 2 months by self-reported EORTC QLQ-C30 questionnaire. Physical dimension scores were analyzed. After multiple imputation of missing data, 27 statistical measures aiming to describe the variation of QoL measures among follow-up were computed for each patient. Based on these measures, patient were grouped into homogenous groups in terms of QoL variation pattern using a K-Means classification method. The mean QoL score at each time was graphically represented in each obtained pattern. Finally, clinical characteristic of patients in each pattern of QoL were described and compared.ResultsThe trial included 416 patients and 1023 questionnaire were collected. 74 % of patients were male with a mean ± SD age of 62 ± 11 years. 43 % of scores were missing. Patients were grouped into four classes of homogeneous QoL variation patterns. 1) a Pattern of 24 (6 %) patients showing improvement in QoL with a mean variation of +10.7 points on the 0–100 scale, 2) a Pattern of 171 (41 %) patients showing a stability 3) two Patterns of 78 (19 %) and 143 (34 %) patients respectively showing a deterioration of QoL with a mean variation of −67.2 and −67.6, respectively. There were no difference between patterns in terms of gender or age. Patients within “degradation” pattern had significantly lower performance status (p = 0.015), higher severe after-effects rate (p < 10-3) and death rate (p < 10-3).ConclusionThis work opens up perspectives for longitudinal data analysis with a high probability of missing values while providing a relevant graphical summary. Patterns of QoL evolution with clinical relevance may help to interpret longitudinal QoL data in Cancer studies.

【 授权许可】

CC BY   
© Nuemi et al. 2015