Summary measures and statistics in the analysis of quality of life data: an example from an EORTC–NCIC–SAKK locally advanced breast cancer study

Curran, Desmond; Aaronson, Neil K.; Standaert, Baudouin; Molenberghs, Geert; Therasse, Patrick; Ramirez, Amanda; Koopmanschap, Marc; Erder, Haim; Piccart, Martine

doi:10.1016/s0959-8049(00)00056-3

Cited by 47 publications

(27 citation statements)

References 20 publications

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“…The RAND36 measures aspects of health relevant to functional status and well-being [66] and is valid/reliable (α = 0.75−0.91) in breast cancer [67][68][69]. Self-reported responses yield four physical and four mental subscales (scored 0−100: higher scores = better health).…”

Section: Psychosocial Functioningmentioning

confidence: 99%

The relative importance of specific risk factors for insomnia in women treated for early‐stage breast cancer

et al. 2007

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Section: Psychosocial Functioningmentioning

confidence: 99%

The relative importance of specific risk factors for insomnia in women treated for early‐stage breast cancer

et al. 2007

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“…As a summary measure of variable pain for every patient, an area under curve (AUC) was calculated according to the trapezium rule. 14 High-dose chemotherapy and transplantation. All patients received a high-dose chemotherapy schedule (STAMP-V): cyclophosphamide 6000 mg/m 2 , thiotepa 500 mg/m 2 and carboplatin 800 mg/m 2 as continuous infusion over 96 h from days À7 through À3.…”

Section: Methodsmentioning

confidence: 99%

A randomised study of 10 μg/kg/day (single dose) vs 2 × 5 μg/kg/day (split dose) G-CSF as stem cell mobilisation regimen in high-risk breast cancer patients

Carrión

Serrano

Gomez-Pineda

et al. 2003

Bone Marrow Transplant

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“…AUC has not been investigated using simulated data informed by PRO questionnaires, although some have considered single PRO data sets (Curran et al, 2000; D. L. Fairclough, 1997;Qian et al, 2000). R. J.…”

Section: Literaturementioning

confidence: 99%

Bias in Area Under the Curve for Longitudinal Clinical Trials With Missing Patient Reported Outcome Data

2014

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A common approach to the analysis of longitudinal patient reported outcomes (PROs) is the use of summary measures such as area under the time curve (AUC). However, it is not clear how missing data affects the validity of AUC analysis. This study aimed to compare the use of AUC summary measures (in individuals) with AUC summary statistics (on groups, calculated from the estimated parameters of a mixed model) when data are complete, missing at random, and missing not at random. A simulation experiment based on a two-armed randomized trial was carried out to investigate the precision and bias of AUC in longitudinal analysis where missingness, trajectory, and missingness allocation were varied. Summary measures AUC with ad hoc approaches to missing data were compared with mixed model AUC summary statistics. AUC summary statistics were consistently superior to AUC summary measures in terms of precision and bias. The bias of AUC summary statistic approach was very small, even when data were missing not at random and when differential attrition between groups existed. AUC summary measures on individuals should not be used to analyze longitudinal PRO data in the presence of missing data.

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Summary measures and statistics in the analysis of quality of life data: an example from an EORTC–NCIC–SAKK locally advanced breast cancer study

Cited by 47 publications

References 20 publications

The relative importance of specific risk factors for insomnia in women treated for early‐stage breast cancer

The relative importance of specific risk factors for insomnia in women treated for early‐stage breast cancer

A randomised study of 10 μg/kg/day (single dose) vs 2 × 5 μg/kg/day (split dose) G-CSF as stem cell mobilisation regimen in high-risk breast cancer patients

Bias in Area Under the Curve for Longitudinal Clinical Trials With Missing Patient Reported Outcome Data

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