Dataset Information

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

louqman@gmail.com

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gaye A

PROVIDER: S-EPMC4528636 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Gaye Amadou A Burton Thomas W Y TW Burton Paul R PR

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]

PMID: 25908791

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Project description:BackgroundDuring the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that many patients were not treated promptly or effectively. However, many unexplained deaths were subsequently identified as cases, indicating a few undetected cases, resulting in a higher estimate of FR. Whether the true FR is exceedingly high and what factors determine the detection of cases remain unknown. Estimating the true number of total infected cases (i.e. including undetected cases) can allow an accurate estimation of FR and effective reproduction number ([Formula: see text]).MethodsWe aimed at quantifying the time-varying FR and [Formula: see text] using the estimated true numbers of cases; and, exploring the relationship between the true case number and test and trace data. After adjusting for reporting delays, we developed a model to estimate the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and [Formula: see text] were calculated using the true number of cases. Afterwards, a logistic regression model was used to assess the impact of daily testing and tracing data on the detection ratio of deaths.ResultsThe estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterwards. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. [Formula: see text] reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was found to be associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact traced before symptom onset.ConclusionsIncreasing testing capacity and contact tracing coverage without delays not only improve parameter estimation by reducing hidden cases but may also reduce fatality rates.

Dataset Information

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

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

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

Similar Datasets

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