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Health and Quality of Life Outcomes BioMed Central Research Open Access Sample size and power

Health and Quality of Life Outcomes BioMed Central Research Open Access Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36 Stephen J Walters* Address: Sheffield Health Economics Group, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent St, Sheffield, United Kingdom, S1 4DA Email: Stephen J Walters* - s.j.walters@shef.ac.uk * Corresponding author Published: 25 May 2004 Health and Quality of Life Outcomes 2004, 2:26 This article is available from: http://www.hqlo.com/content/2/1/26 Received: 16 April 2004 Accepted: 25 May 2004 © 2004 Walters; licensee BioMed Central Ltd. This is an Open Access article: verbatim. | BioMed Central Health and Quality of Life Outcomes Research Open Access Sample size and power estimation for studies with health related quality of life outcomes a comparison of four methods using the Sf-36 Stephen J Walters Address Sheffield Health Economics Group School of Health and Related Research University of Sheffield Regent Court 30 Regent St Sheffield United Kingdom S1 4DA Email Stephen J Walters - s.j.walters@shef.ac.uk Corresponding author Published 25 May 2004 Received 16 April 2004 Accepted 25 May 2004 Health and Quality of Life Outcomes 2004 2 26 This article is available from http www.hqlo.cOm content 2 1 26 2004 Walters licensee BioMed Central Ltd. This is an Open Access article verbatim copying and redistribution of this article are permitted in all media for any purpose provided this notice is preserved along with the article s original URL. Abstract We describe and compare four different methods for estimating sample size and power when the primary outcome of the study is a Health Related Quality of Life HRQoL measure. These methods are 1. assuming a Normal distribution and comparing two means 2. using a non-parametric method 3. Whitehead s method based on the proportional odds model 4. the bootstrap. We illustrate the various methods using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness or superiority of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values 7 and or the expected proportion of cases at the boundaries is high scoring 0 or 100 then we would recommend using Whitehead s method Method 3 . Alternatively if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low then we would recommend using Method 1. If a pilot or historical dataset is readily available to estimate the shape of the distribution then bootstrap simulation Method 4 .