--- title: "AE Summary" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{AE Summary} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include=FALSE} knitr::opts_chunk$set( comment = "#>", collapse = TRUE, out.width = "100%", dpi = 150 ) ``` ```{r} library(metalite.ae) ``` ## Overview The objective of this tutorial is to generate a production-ready AE summary. It extends examples shown in the [AE summary chapter](https://r4csr.org/tlf-ae-summary.html) of the _R for Clinical Study Reports and Submission_ book. The AE summary analysis entails the creation of tables that summarize adverse events information. To accomplish this using metalite.ae, three essential functions are required: - `prepare_ae_summary()`: prepare analysis raw datasets. - `format_ae_summary()`: prepare analysis (mock) outdata with proper format. - `tlf_ae_summary()`: transfer (mock) output dataset to RTF files. There is one optional function to extend AE summary analysis: - `extend_ae_specific_inference()`: add risk difference inference results based on M&N method. An example output: ```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"} knitr::include_graphics("pdf/ae0summary1.pdf") ``` ## Example data Within metalite.ae, we utilized the ADSL and ADAE datasets from the metalite package to create an illustrative dataset. The metadata structure remains consistent across all analysis examples within metalite.ae. Additional information can be accessed on the [metalite package website](https://merck.github.io/metalite/articles/metalite.html). ```{r} meta <- meta_ae_example() ```
Click to show the output ```{r} meta ```
## Analysis preparation The function `prepare_ae_summary()` is used to create a dataset for AE summary analysis by utilizing predefined keywords specified in the example data `meta`. The resulting output of the function is an outdata object, which comprises a collection of raw datasets for analysis and reporting. ```{r, message=FALSE} outdata <- prepare_ae_summary( meta, population = "apat", observation = "wk12", parameter = "any;rel;ser" ) ``` ```{r} outdata ``` The resulting dataset contains frequently used statistics, with variables indexed according to the order specified in `outdata$group`. ```{r} outdata$group ``` The row is indexed according to the order of `outdata$name`. ```{r} head(data.frame(outdata$order, outdata$name)) ``` - `n_pop`: number of participants in population. ```{r} outdata$n_pop ``` - `n`: number of subjects with AE. ```{r} head(outdata$n) ``` - `prop`: proportion of subjects with AE. ```{r} head(outdata$prop) ``` - `diff`: risk difference compared with the `reference_group`. ```{r} head(outdata$diff) ``` ## Format output Once the raw analysis results are obtained, the `format_ae_summary()` function can be employed to prepare the outdata, ensuring its compatibility with production-ready RTF tables. ```{r} tbl <- outdata |> format_ae_summary() tbl$tbl ``` ### Additional statistics By using the `display` argument, we can choose specific statistics to include. For instance, we have the option to incorporate the risk difference. ```{r} tbl <- outdata |> format_ae_summary(display = c("n", "prop", "diff")) tbl$tbl ``` To perform advanced analysis, the `extend_ae_specific_inference()` function is utilized. For instance, we can incorporate a 95% confidence interval based on the Miettinen and Nurminen (M&N) method. Further information regarding the M&N method can be found in the [rate compare vignette](https://merck.github.io/metalite.ae/articles/rate-compare.html). ```{r} tbl <- outdata |> extend_ae_specific_inference() |> format_ae_summary(display = c("n", "prop", "diff", "diff_ci")) tbl$tbl ``` ### Mock data preparation The `mock` argument facilitates the creation of a mock table with ease. Please note that the intention of the `mock` argument is not to provide an all-encompassing mock table template. Instead, it serves as a convenient method to assist users in generating a mock table that closely resembles the desired output layout. To develop a more versatile mock table generation tool, further efforts are necessary. This could potentially involve the creation of a dedicated mock table generation package or similar solutions. ```{r} tbl <- outdata |> format_ae_summary(mock = TRUE) tbl$tbl ``` ## RTF tables The last step is to prepare the RTF table using `tlf_ae_summary()`. ```{r} outdata |> format_ae_summary() |> tlf_ae_summary( source = "Source: [CDISCpilot: adam-adsl; adae]", analysis = "ae_summary", # Provide analysis type defined in meta$analysis path_outtable = "rtf/ae0summary1.rtf" ) ``` ```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"} knitr::include_graphics("pdf/ae0summary1.pdf") ``` The `tlf_ae_summary()` function also provides some commonly used argument to customize the table. ```{r} outdata |> format_ae_summary() |> tlf_ae_summary( source = "Source: [CDISCpilot: adam-adsl; adae]", analysis = "ae_summary", # Provide analysis type defined in meta$analysis col_rel_width = c(6, rep(1, 8)), text_font_size = 8, orientation = "landscape", path_outtable = "rtf/ae0summary2.rtf" ) ``` ```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"} knitr::include_graphics("pdf/ae0summary2.pdf") ``` The empty table can be generated if there is not result to display. ```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"} knitr::include_graphics("pdf/empty_ae0specific.pdf") ``` The mock table can also be generated. ```{r} outdata |> format_ae_summary(mock = TRUE) |> tlf_ae_summary( source = "Source: [CDISCpilot: adam-adsl; adae]", analysis = "ae_summary", # Provide analysis type defined in meta$analysis path_outtable = "rtf/mock_ae0summary1.rtf" ) ``` ```{r, out.width = "100%", out.height = "400px", echo = FALSE, fig.align = "center"} knitr::include_graphics("pdf/mock_ae0summary1.pdf") ```