ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies

Amadou Gaye; Thomas W Y Burton; Paul R Burton

doi:10.1093/bioinformatics/btv219

ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies

Amadou Gaye, Thomas W Y Burton, Paul R Burton

Bristol Medical School (PHS)

Research output: Contribution to journal › Article (Academic Journal) › peer-review

8 Citations (Scopus)

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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Original language	English
Journal	Bioinformatics
DOIs	https://doi.org/10.1093/bioinformatics/btv219
Publication status	Published - 22 Apr 2015

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title = "ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies",

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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.",

author = "Amadou Gaye and Burton, {Thomas W Y} and Burton, {Paul R}",

note = "{\textcopyright} The Author 2015. Published by Oxford University Press.",

year = "2015",

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doi = "10.1093/bioinformatics/btv219",

language = "English",

journal = "Bioinformatics",

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T1 - ESPRESSO

T2 - taking into account assessment errors on outcome and exposures in power analysis for association studies

AU - Gaye, Amadou

AU - Burton, Thomas W Y

AU - Burton, Paul R

PY - 2015/4/22

Y1 - 2015/4/22

N2 - 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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

AB - 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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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DO - 10.1093/bioinformatics/btv219

M3 - Article (Academic Journal)

C2 - 25908791

SN - 1367-4811

JO - Bioinformatics

JF - Bioinformatics

ER -

ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies

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