Dataset Information
ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies.
Ontology highlight
ABSTRACT: Motivation
Very large studies are required to provide sufficiently big sample sizes for adequately powered association analyses. This can be an expensive undertaking and it is important that an accurate sample size is identified. For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account. Conventional methods to analyse power use closed-form solutions that are not flexible enough to cater for all of these elements easily. They often result in a potentially substantial overestimation of the actual power.Results
In this article, we describe the Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes tool that allows assessment errors in power calculation under various biomedical scenarios to be incorporated. We also report a real world analysis where we used this tool to answer an important strategic question for an existing cohort.Availability and implementation
The software is available for online calculation and downloads at http://espresso-research.org. The code is freely available at https://github.com/ESPRESSO-research.Contact
louqman@gmail.comSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Gaye A
PROVIDER: S-EPMC4528636 | biostudies-literature | 2015 Aug
REPOSITORIES: biostudies-literature
Publications
ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies.
Bioinformatics (Oxford, England) 20150422 16
<h4>Motivation</h4>Very large studies are required to provide sufficiently big sample sizes for adequately powered association analyses. This can be an expensive undertaking and it is important that an accurate sample size is identified. For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account. Conventional methods to analyse power use clo ...[more]
