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01.12.2014 | Research article | Ausgabe 1/2014 Open Access

BMC Medical Research Methodology 1/2014

A simple ratio-based approach for power and sample size determination for 2-group comparison using Rasch models

BMC Medical Research Methodology > Ausgabe 1/2014
Véronique Sébille, Myriam Blanchin, Francis Guillemin, Bruno Falissard, Jean-Benoit Hardouin
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1471-2288-14-87) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

VS, JBH and MB have made substantial contributions to conception and design, analysis and interpretation of data; BF and FG has been involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript.



Despite the widespread use of patient-reported Outcomes (PRO) in clinical studies, their design remains a challenge. Justification of study size is hardly provided, especially when a Rasch model is planned for analysing the data in a 2-group comparison study. The classical sample size formula (CLASSIC) for comparing normally distributed endpoints between two groups has shown to be inadequate in this setting (underestimated study sizes). A correction factor (RATIO) has been proposed to reach an adequate sample size from the CLASSIC when a Rasch model is intended to be used for analysis. The objective was to explore the impact of the parameters used for study design on the RATIO and to identify the most relevant to provide a simple method for sample size determination for Rasch modelling.


A large combination of parameters used for study design was simulated using a Monte Carlo method: variance of the latent trait, group effect, sample size per group, number of items and items difficulty parameters. A linear regression model explaining the RATIO and including all the former parameters as covariates was fitted.


The most relevant parameters explaining the ratio’s variations were the number of items and the variance of the latent trait (R2 = 99.4%).


Using the classical sample size formula adjusted with the proposed RATIO can provide a straightforward and reliable formula for sample size computation for 2-group comparison of PRO data using Rasch models.
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