README.Rmd

---
output: github_document
link-citation: yes
pkgdown:
   as_is:true
references:
- id: tang2017
  title: "Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout"
  author:
  - family: Tang
    given: Y
  container-title: Statistics in Medicine
  volume: 37
  URL: 'https://dx.doi.org/10.1002/sim.7583'
  DOI: 10.1002/sim.7583
  issue: 9
  page: 1467 -- 81
  type: article-journal
  issued:
    year: 2018
- id: Erler2021
  title: "JointAI: Joint Analysis and Imputation of Incomplete Data in R"
  author:
  - family: Erler
    given: NS
  - family: Rizopoulos
    given: D
  - family: Lesaffre
    given: EMEH
  container-title: Journal of Statistical Software
  volume: 100
  URL: 'https://dx.doi.org/10.18637/jss.v100.i20'
  DOI: 10.18637/jss.v100.i20
  issue: 20
  page: 1 -- 56
  type: article-journal
  issued:
    year: 2021
- id: wang2022
  title: "Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness"
  author:
  - family: Wang
    given: T
  - family: Liu
    given: Y
  container-title: arXiv
  volume: 2203.02771
  URL: 'https://arxiv.org/pdf/2203.02771'
  type: article-journal
  issued:
    year: 2022    
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

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

# <span style="color: blue;">remiod</span>: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness 

<!-- badges: start -->
[![CRAN Status](https://www.r-pkg.org/badges/version/remiod)](https://CRAN.R-project.org/package=remiod)
[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/remiod)](https://cran.r-project.org/package=remiod)
[![GPL-3.0](https://img.shields.io/github/license/xsswang/remiod?logo=GNU&logoColor=FFFFFF&style=flat-square)](https://github.com/xsswang/remiod/main/LICENSE)
[![R build
status](https://github.com/xsswang/remiod/workflows/R-CMD-check/badge.svg)](https://github.com/xsswang/remiod/actions)
<!-- badges: end -->

The package **remiod** provides functionality to perform controlled multiple
imputation of binary and ordinal response in the Bayesian framework. Implemented are
(generalized) linear regression models for binary data and cumulative logistic models for
ordered categorical data [@wang2022]. It is also possible to fit multiple models of mixed types
simultaneously. Missing values in (if present) will be imputed automatically.

**remiod** has two algorithmic backend. One is [JAGS](https://mcmc-jags.sourceforge.io/), with which the function performs some preprocessing of the data and creates a JAGS model, which will then automatically be
passed to [JAGS](https://mcmc-jags.sourceforge.io/) with the help of the R package [**rjags**](https://CRAN.R-project.org/package=rjags). The another is based on the method proposed by Tang [@tang2017].

Besides the main modelling functions, **remiod** also provides functions to summarize and visualize results.

    
## Installation

**remiod** Can be from [CRAN](https://cran.r-project.org/web/packages/remiod/index.html):
```{r cran-install, eval = FALSE}
install.packages("remiod")
```
Or, it can be installed from GitHub:
```{r gh-installation, eval = FALSE}
# install.packages("remotes")
remotes::install_github("xsswang/remiod")
```


## Main functions
**remiod** provides the following main functions:

``` r
remiod                      #processing data and implementing MCMC sampling
extract_MIdata              #extract imputed data sets
miAnalyze                   #Perform analyses using imputed data and pool results
```

Currently, methods **remiod** implements include  missing at random (`MAR`), jump-to-reference (`J2R`), copy reference (`CR`), and delta adjustment (`delta`). For `method = "delta"`, argument `delta` should follow to specify a numerical values used in delta adjustment. These methods can be requested through `extract_MIdata()`, and imputed datasets can be analyzed using `miAnalyze()`.

Functions `summary()`, `coef()`, and `mcmcplot()` provide a summary of the posterior distribution under MAR and its visualization.




## Minimal Example

```{r, eval = FALSE}

data(schizow)

test = remiod(formula = y6 ~ tx + y0 + y1 + y3, data = schizow,
              trtvar = 'tx', algorithm = 'jags', method="MAR",
              ord_cov_dummy = FALSE, n.adapt = 10, n.chains = 1,
              n.iter = 100, thin = 2, warn = FALSE, seed = 1234)

extdt = extract_MIdata(object=test, method="J2R",mi.setting=NULL, M=10, minspace=2)
result = miAnalyze(y6 ~ y1 + tx, data = extdt, pool = TRUE)

```

## Support
For any help with regards to using the package or if you find a bug please create a [GitHub issue](https://github.com/xsswang/remiod/issues).

## Reference