Quality of Life (QL) is now included as an endpoint in many phase III cancer clinical trials. Numerous statistical techniques have been presented in the literature to analyse QL data but there is still no agreement as to what is the optimal approach of analysis. In this paper we, therefore, present and compare various techniques which have all appeared in the literature and which may be globally described as summary measures and summary statistics. These techniques are illustrated using data from an EORTC clinical trial in locally advanced breast cancer (EORTC trial 10921). It is also explained in this paper how and when these techniques may be used in other cancer settings. For EORTC trial 10921, it is shown that by choosing different techniques different conclusions may be drawn concerning the QL outcome. This highlights the importance of choosing an appropriate primary statistical method and for describing it a priori in the protocol and analysis plan. In this paper, we show the importance of performing sensitivity or supportive analysis to support conclusions drawn from the primary analysis. Copyright (C) 2000 Elsevier Science Ltd.

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doi.org/10.1016/S0959-8049(00)00056-3, hdl.handle.net/1765/69077
European Journal of Cancer
Erasmus School of Health Policy & Management (ESHPM)

Curran, D, Aaronson, N.K, Standaert, B, Molenberghs, G, Therasse, P, Ramirez, A.J, … Piccart, M.J. (2000). Summary measures and statistics in the analysis of quality of life data: An example from an EORTC-NCIC-SAKK locally advanced breast cancer study. European Journal of Cancer, 36(7), 834–844. doi:10.1016/S0959-8049(00)00056-3