Abstract
Early stopping of clinical trials either in the case of beneficial or deleterious effect of treatment on quality of life (QoL) is an important issue. QoL is usually evaluated using self-assessment questionnaires and responses to the items are combined into scores assumed to be normally distributed (which is rarely the case). An alternative is to use item response theory (IRT) models such as the partial credit model (PCM) for polytomous items which takes into account the categorical nature of the items.
Sequential analysis and mixed partial credit models were combined in the context of phase II noncomparative trials. The statistical properties of the sequential probability ratio test (SPRT) and of the triangular test (TT) were compared using mixed PCM and traditional average scores methods (ASM) by means of simulations.
The type I error of the sequential tests was correctly maintained for both methods, the mixed PCM being more conservative than the ASM. Although remaining a bit underpowered, the mixed PCM displayed higher power than the ASM for both sequential tests. Both methods allowed substantial reductions in average sample numbers as compared with fixed sample designs. Overlapping of item category particularly affected the ASM by inflating the type I error and power. The use of IRT models in sequential analysis of QoL endpoints is promising and should provide a more powerful method to detect therapeutic effects than the traditional ASM.
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Sébille, V., Challa, T., Mesbah, M. (2007). Sequential Analysis of Quality of Life Measurements with the Mixed Partial Credit Model. In: Auget, JL., Balakrishnan, N., Mesbah, M., Molenberghs, G. (eds) Advances in Statistical Methods for the Health Sciences. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4542-7_7
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DOI: https://doi.org/10.1007/978-0-8176-4542-7_7
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