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README.Rmd
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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## mult_sampsize
## [](https://github.com/dwyl/esta/issues)
## Description
R package for determining sample size required in studies using composite endpoints with mixed continuous and discrete components analysed using the augmented binary method.
## Getting started
Install from Github using `devtools::install_github("martinamcm/mult_sampsize")`
Implementation as a Shiny app with further documentation on functionality and examples available at [MultSampSize](https://github.com/martinamcm/MultSampSize)
## Details
### Composite Endpoints
Function `sampsizecomp()` provides the required sample size using both the augmented binary method
based on a latent variable model and a standard binary method based on a logistic regression model
with the following arguments:
* `augmean` mean risk difference treatment effect estimated using augmented binary method
* `binmean` mean risk difference treatment effect estimated using standard binary method
* `augvar` variance of risk difference treatment effect estimated using augmented binary method
* `binvar` variance of risk difference treatment effect estimated using standard binary method
* `alpha` one-sided alpha level
* `beta` beta level: 1-desired power
Estimates of these quantities can be obtained from existing data using the `augbin_rheum` package, as shown below.
#### Example
Assuming the endpoint of interest is a composite endpoint comprised of two continuous and one binary component,
the sample size required in each arm when the dichotomisation thresholds are equal to 18 and 6 is obtained as below.
More details and `Egdata21` can be obtained from [MultSampSize](https://github.com/martinamcm/MultSampSize).
```{r, eval=F}
devtools::install_github("martinamcm/augbin_rheum")
data_fit <- augbinrheum(Egdata21,2,1,c(18,6))
augmean_est <- data_fit$risk_diff$est[1]
binmean_est <- data_fit$risk_diff$est[2]
augvar_est <- ((data_fit$risk_diff$ci_upper[1]-augmean_est)/1.96)^2
binvar_est <- ((data_fit$risk_diff$ci_upper[2]-binmean_est)/1.96)^2
sampsizecomp(augmean_est,binmean_est,augvar_est,binvar_est,0.05,0.2)
```
### References
McMenamin M, Barrett JK, Berglind A, Wason JMS. Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints. *arXiv*. 2019. [arXiv:1912.05258](https://arxiv.org/abs/1912.05258).
McMenamin M, Grayling MJ, Berglind A, Wason JMS. Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial. *medRxiv*. 2020. doi: [10.1101/2020.07.28.20163378](https://www.medrxiv.org/content/10.1101/2020.07.28.20163378v1)
McMenamin M, Barrett JK, Berglind A, Wason JM. Employing a latent variable framework to improve efficiency in composite endpoint analysis. *Statistical Methods in Medical Research*. 2021;30(3):702-716. doi: [10.1177/0962280220970986](https://doi.org/10.1177/0962280220970986)