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stdreg2

Regression Standardization for Causal Inference

https://github.com/sachsmc/stdreg2

Science Score: 13.0%

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Repository

Regression Standardization for Causal Inference

Basic Info
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  • Stars: 4
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License

README.Rmd 

---
output: github_document
editor_options: 
  chunk_output_type: console
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# stdReg2: Regression Standardization for Causal Inference




Goals: create a unified interface for regression standardization to obtain estimates of causal effects such as the average treatment effect, or relative treatment effect. 

1. Should be easy to use for applied practitioners, i.e., as easy as running glm or coxph. 
2. We want to implement modern, theoretically grounded, doubly-robust estimators, and their associated variance estimators.
3. We want it to be extensible for statistical researchers, i.e., possible to implement new estimators and get other models used within the interface.
4. Robust and clear documentation with lots of examples and explanation of the necessary assumptions. 

## Difference between `stdReg2` and `stdReg`

`stdReg2` is the next generation of `stdReg`. If you are happy using `stdReg`, you can continue using it and nothing will change in the near future. With `stdReg2` we aim to solve similar problems but with nicer output, more available methods, the possibility to include new methods, and mainly to make maintenance and updating easier. 

## Installation

`stdReg2` is available on CRAN and can be installed with: 
```r
install.packages("stdReg2")
```


You can install the development version of `stdReg2` from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("sachsmc/stdReg2")
```

## Example

This is a basic example which shows you how to use regression standardization in a logistic regression model to obtain estimates of the causal risk difference and causal risk ratio:

```{r example}
library(stdReg2)

# basic example
# need to correctly specify the outcome model and no unmeasured confounders
# (+ standard causal assumptions)
set.seed(6)
n <- 100
Z <- rnorm(n)
X <- rnorm(n, mean = Z)
Y <- rbinom(n, 1, prob = (1 + exp(X + Z))^(-1))
dd <- data.frame(Z, X, Y)
x <- standardize_glm(
 formula = Y ~ X * Z,
 family = "binomial",
 data = dd,
 values = list(X = 0:1),
 contrasts = c("difference", "ratio"),
 reference = 0
)
x
plot(x)
tidy(x)
```

For more detailed examples, see the vignette "Estimation of causal effects using stdReg2".


## Citation

```{r}
citation("stdReg2")
```

Owner

  • Name: Michael Sachs
  • Login: sachsmc
  • Kind: user
  • Location: Copenhagen, Denmark
  • Company: University of Copenhagen

GitHub Events

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  • Issues event: 1
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Last synced: 10 months ago

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Top Authors
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  • sachsmc (1)
  • dominicmagirr (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 224 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: stdReg2

Regression Standardization for Causal Inference

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 224 Last month
Rankings
Dependent packages count: 28.2%
Dependent repos count: 34.8%
Average: 49.9%
Downloads: 86.7%
Maintainers (1)
sachsmc@gmail.com
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
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  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests
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