AN EVALUATION OF STATISTICAL METHODS USED TO ANALYSE PATIENT-REPORTED OUTCOMES (PRO) DATA IN PUBLISHED METASTATIC CANCER STUDIES
Author(s)
Gilet H1, Brédart A1, Regnault A1, Bhandary D2, Parasuraman B31Mapi Values, Lyon, France, 2AstraZeneca Pharmaceuticals LP, Wilmington, DE, USA, 3AstraZeneca, Glen Mills, PA, USA
Presentation Documents
OBJECTIVES: As metastatic cancers are generally incurable, treatment goal is to control the cancer and relieve symptoms with minimal side effects, making patient-reported outcomes (PRO) of particular interest in addition to traditional clinical outcomes. The objective of this literature review was to explore and evaluate the PRO data analyses reported in published metastatic cancer studies. METHODS: The literature search was conducted on Medline and Embase databases (1999-2009). The search focused on two types of PRO analyses: the association between PRO scores and clinical outcomes, and the assessment of treatment benefit in terms of PROs. General keywords related to the tumour site and PROs, and keywords specific to each type of analysis were defined. A total of 931 different abstracts were reviewed by one statistician, among which 47 were finally selected for in-depth review based on their relevance to review objectives. RESULTS: The relationship between PRO scores and clinical outcomes was mainly analysed with Cox models, since clinical endpoint was generally survival. When analyses did not involve survival, the association between PRO and clinical outcomes and the use of PRO scores as endpoints were appropriately analysed with various descriptive, non-parametric and parametric statistical methods, depending on parameters like study objectives, design, PRO endpoints used and sample size. Only a few studies discussed the clinical meaningfulness of results alongside statistical significance. CONCLUSIONS: While a clear consistency was found in the statistical method for the analysis of the link between PRO scores and survival measures, a large heterogeneity of statistical methodologies was observed for other types of PRO analysis. In most studies, the method was appropriate from a statistical perspective but not adapted to the specific nature of PRO data, including under-use of clinically meaningful interpretation of statistical results and absence of specific PRO approaches such as cumulative distribution curves.
Conference/Value in Health Info
2011-11, ISPOR Europe 2011, Madrid, Spain
Value in Health, Vol. 14, No. 7 (November 2011)
Code
PCN199
Topic
Methodological & Statistical Research
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
Oncology