metamedian

Meta-Analysis of Medians

https://github.com/stmcg/metamedian

Repository

Meta-Analysis of Medians

README.Rmd

---
output: github_document
---



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

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The `metamedian` package implements methods to meta-analyze studies that report estimates of the median of the outcome of interest. 

## Methods Included

### Median-Based Methods

This package implements several methods to directly meta-analyze studies reporting sample medians. When the primary studies are one-group studies, the methods of [McGrath et al. (2019)](https://doi.org/10.1002/sim.8013) and [Ozturk and Balakrishnan (2020)](https://doi.org/10.1002/sim.8738) can be applied to estimate the pooled median. In the two-group context, the methods of [McGrath et al. (2020a)](https://doi.org/10.1002/bimj.201900036) can be applied to estimate the pooled difference of medians between groups. 

The package also implements methods to meta-analyze median survival times. The package can apply the methods described by [McGrath et al. (2025)](
https://doi.org/10.48550/arXiv.2503.03065) to meta-analyze the median survival time, difference of median survival times between groups, and the ratio of median survival times between groups.

### Mean-Based Methods

These methods estimate the study-specific means and their standard errors from studies reporting sample medians in order to estimate the pooled (difference of) means. Specifically, one can apply the following methods:

- [Hozo et al. (2005)](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-5-13)
- [Wan et al. (2014)](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-135)
- [Bland (2015)](https://lifescienceglobal.com/pms/index.php/ijsmr/article/view/2688)
- [Luo et al. (2018)](https://doi.org/10.1177/0962280216669183)
- [Shi et al. (2020a)](https://doi.org/10.1002/jrsm.1429)
- [Shi et al. (2020b)](https://www.intlpress.com/site/pub/pages/journals/items/sii/content/vols/0013/0004/a009/)
- [McGrath et al. (2020b)](https://doi.org/10.1177/0962280219889080)
- [Cai et al. (2021)](https://doi.org/10.1177/09622802211047348)
- [Yang et al. (2022)](https://doi.org/10.1080/02664763.2021.1967890)
- [McGrath et al. (2023)](https://doi.org/10.1177/09622802221139233)
- [Shi et al. (2023)](https://doi.org/10.1177/09622802231172043)

## Installation

You can install the released version of `metamedian` from CRAN with:

``` r
install.packages("metamedian")
```

After installing the `devtools` package (i.e., calling `install.packages(devtools)`), the development version of `metamedian` can be installed from GitHub with:
``` r
devtools::install_github("stmcg/metamedian")
```

## Usage

See [McGrath et al. (2024)](https://doi.org/10.1002/jrsm.1686) for a detailed guide on using the `metamedian` package. Below, we include the code used in the main examples in the paper. The examples are based on the dataset `dat.age` included in the package.

Loading the package:

``` {r}
library(metamedian)
```

Setting a random number seed for reproducibility:

``` {r}
set.seed(1234)
```

### Descriptive Analyses

```{r}
describe_studies(data = dat.age, group_labels = c ("Nonsurvivors", "Survivors"))
```

### Mean-Based Methods

The analyses below may take 5-10 minutes to run on a standard laptop. For faster runtime, consider using fewer bootstrap replicates for the QE, BC, and MLN methods.

```{r}
res_wan <- metamean(dat.age, mean_method = "hozo/wan/bland")
res_luo <- metamean(dat.age, mean_method = "luo")
res_shi <- metamean(dat.age, mean_method = "shi_lognormal")
res_qe_mean <- metamean(dat.age, mean_method = "qe")
res_bc <- metamean(dat.age, mean_method = "bc")
res_mln <- metamean(dat.age, mean_method = "mln")
res_yang <- metamean(dat.age, mean_method = "yang")
```

The output of `res_mln` is illustrated below:
```{r}
res_mln
```

### Median-Based Methods
```{r}
res_mm <- metamedian(dat.age, median_method = "mm")
res_wm <- metamedian(dat.age, median_method = "wm")
res_qe_median <- metamedian(dat.age, median_method = "qe")
```

The output of `res_qe_median` is illustrated below:
```{r}
res_qe_median
```

## Reference

To cite this package, use: 

> McGrath S, Zhao X, Ozturk O, Katzenschlager S, Steele R, Benedetti A. metamedian: An R package for meta-analyzing studies reporting medians. *Research Synthesis Methods*. 2024; 15(2):332-346. doi:10.1002/jrsm.1686

For a BibTeX citation, use: 

```
@article{mcgrath2024metamedian,
  title={metamedian: An R package for meta-analyzing studies reporting medians},
  author={McGrath, Sean and Zhao, XiaoFei and Ozturk, Omer and Katzenschlager, Stephan and Steele, Russell and Benedetti, Andrea},
  journal={Research Synthesis Methods},
  volume={15},
  number={2},
  pages={332--346},
  year={2024},
  publisher={Wiley Online Library}
}
```

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