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mecor

measurement error correction

https://github.com/lindanab/mecor

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
  • ✓
    DOI references
    Found 21 DOI reference(s) in README
  • ○
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    Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

linear-models measurement-error statistics
Last synced: 6 months ago · JSON representation

Repository

measurement error correction

Basic Info
  • Host: GitHub
  • Owner: LindaNab
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 1.42 MB
Statistics
  • Stars: 6
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
linear-models measurement-error statistics
Created about 8 years ago · Last pushed about 4 years ago
Metadata Files
Readme

README.Rmd 

---
output:
  md_document:
    variant: markdown_github
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# The mecor Package
This package for R implements measurement error correction methods for 
measurement error in a continuous covariate or outcome in a linear model 
with a continuous outcome. 

# Installation
The package can be installed via
```r
devtools::install_github("LindaNab/mecor", build_vignettes = TRUE)
```

# Quick demo
```r
library(mecor)
# load the internal covariate validation study
data("icvs", package = "mecor")
head(icvs)
# correct the biased exposure-outcome association
mecor(Y ~ MeasError(X_star, reference = X) + Z, data = icvs, method = "standard")
```

# More examples
Browse the vignettes of the package for more information.
```r
browseVignettes(package = "mecor")
```


# References
## Key reference
- Nab L, van Smeden M, Keogh RH, Groenwold RHH. mecor: an R package for
measurement error correction in linear models with a continuous outcome.

## References to methods implemented in the package
- Bartlett JW, Stavola DBL, Frost C. Linear mixed models for replication data to efficiently allow for covariate measurement error. Statistics in Medicine. 2009:28(25):3158–3178. [doi:10.1002/sim.3713](https://doi.org/10.1002/sim.3713)

- Buonaccorsi JP. Measurement error: Models, methods, and applications. 2010. Chapman & Hall/CRC, Boca Raton.

- Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in non-linear models: A modern perspective. 2006, 2nd edition. Chapman & Hall/CRC, Boca Raton.

- Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI, Freedman LS. Statistical issues related to dietary intake as the response variable in intervention trials. Statistics in Medicine. 2016:35(25):4493–4508. [doi:10.1002/sim.7011](https://doi.org/10.1002/sim.7011)

- Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Statistics in Medicine 2014:33(12):2137–2155. [doi:10.1002/sim.6095](https://doi.org/10.1002/sim.6095)

- Nab L, Groenwold RHH, Welsing PMJ, van Smeden M. Measurement error 
in continuous endpoints in randomised trials: Problems and solutions. Statistics
in Medicine. 2019:38(27):5182-5196. [doi:10.1002/sim.8359](https://doi.org/10.1002/sim.8359)

- Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: The case of multiple covariates measured with error. 1990:132(4):734-745. [doi:10.1093/oxfordjournals.aje.a115715](https://doi.org/10.1093/oxfordjournals.aje.a115715)

- Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for random within-person measurement error. American Journal of Epidemiology. 1992:136(11):1400-1413. [doi:10.1093/oxfordjournals.aje.a116453](https://doi.org/10.1093/oxfordjournals.aje.a116453)

- Spiegelman D, Carroll RJ, Kipnis V. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Statistics in Medicine. 2001:20(1):139-160. [doi:10.1002/1097-0258(20010115)20:1<139::AID-SIM644>3.0.CO;2-K](https://doi.org/10.1002/1097-0258(20010115)20:1<139::AID-SIM644>3.0.CO;2-K)

Owner

  • Name: Linda Nab
  • Login: LindaNab
  • Kind: user
  • Location: Oxford
  • Company: University of Oxford @ebmdatalab @opensafely

I am an epidemiologist working at the University of Oxford @ebmdatalab @opensafely

GitHub Events

Total
Last Year

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 134
  • Total Committers: 3
  • Avg Commits per committer: 44.667
  • Development Distribution Score (DDS): 0.142
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Linda Nab l****b@l****l 115
Linda Nab L****b@l****l 10
BasPdV 4****V 9
Committer Domains (Top 20 + Academic)
lumc.nl: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 14
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Total issue authors: 0
  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • LindaNab (7)
  • BasPdV (7)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 244 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: mecor

Measurement Error Correction in Linear Models with a Continuous Outcome

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 244 Last month
Rankings
Dependent repos count: 19.2%
Stargazers count: 20.6%
Forks count: 21.0%
Average: 28.2%
Dependent packages count: 28.7%
Downloads: 51.7%
Maintainers (1)
lindanab4@gmail.com
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • lme4 * imports
  • lmerTest * imports
  • numDeriv * imports
  • knitr * suggests
  • rmarkdown * suggests