-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
90fbc3b
commit 4d3a7fb
Showing
62 changed files
with
21,595 additions
and
73 deletions.
There are no files selected for viewing
68 changes: 68 additions & 0 deletions
68
...0-09-14-effect-sizes-in-single-subject-designs/effect-sizes-in-single-subject-designs.Rmd
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| --- | ||
| title: "Effect Sizes in single-subject designs" | ||
| author: Rob Cavanaugh | ||
| date: 09-14-2020 | ||
| output: | ||
| distill::distill_article: | ||
| self_contained: false | ||
| --- | ||
|
|
||
|
|
||
| ```{r setup, include=FALSE} | ||
| knitr::opts_chunk$set(echo = FALSE) | ||
| knitr::opts_chunk$set(collapse = TRUE) | ||
| knitr::opts_chunk$set(dev.args=list(bg="transparent")) | ||
| ``` | ||
|
|
||
|
|
||
| ```{r, message = F, warning = F, echo = F} | ||
| library(ggplot2) | ||
| library(dplyr) | ||
| library(see) | ||
| library(readr) | ||
| library(here) | ||
| df <- read_csv(here('data', 'shiny_data.csv')) %>% | ||
| mutate(sub_id = as.factor(sub_id), | ||
| phase = as.factor(phase)) | ||
| ``` | ||
| ```{r, message = F, warning = F, echo = F} | ||
| library(showtext) | ||
| font_add_google(name = "Roboto", family = "roboto", | ||
| regular.wt = 300, bold.wt = 500) | ||
| showtext_auto() | ||
| showtext_opts(dpi = 112) | ||
| ``` | ||
| People with aphasia respond in very different way to treatment. Changes can be immediate or delayed, fast or slow. Some people don't benefit at all. Measuring how much people with aphasia benefit from a treatment is important for justifying clinical services and accurately modeling predictors of treatment outcomes | ||
|
|
||
| In _A Systematic Appraisal of Effect Sizes in Aphasia Single-Case Design via Simulation_, we simulated data for 100 hypothetical people with aphasia who received a naming treatment in a multiple-baseline design. Then we compared different effect size measures that have been used in the aphasia single-case design literature. spoiler: they're not all the same. | ||
|
|
||
| <a href="https://rb-cavanaugh.shinyapps.io/scrollytell/" target="_blank"> Read more here</a> | ||
|
|
||
|
|
||
| ```{r plot, echo = F} | ||
| df %>% | ||
| ggplot(aes(x = session, y = mean_correct, shape = phase, color = sub_id,)) + | ||
| geom_point(size = 4) + | ||
| geom_line(size = 1.5) + | ||
| geom_vline(aes(xintercept = 5.5), alpha = .5) + | ||
| scale_y_continuous(limits = c(0,1), labels = scales::percent) + | ||
| scale_x_continuous(labels = seq(1,15,1), breaks = seq(1,15,1)) + | ||
| theme_modern(base_size = 14) + | ||
| theme(legend.position = 'none', | ||
| panel.background = element_rect(fill = "transparent",colour = NA), | ||
| panel.grid.minor = element_blank(), | ||
| panel.grid.major = element_blank(), | ||
| plot.background = element_rect(fill = "transparent",colour = NA), | ||
| axis.title.x = element_text(size = 16, family = 'roboto', color = 'black'), | ||
| axis.title.y = element_text(size = 16, family = 'roboto', color = 'black'), | ||
| axis.text = element_text(color = 'black', family = 'roboto')) + | ||
| scale_color_brewer(type='qual', palette = 2) + | ||
| ylab('Accuracy') + | ||
| xlab("Session") | ||
| ``` | ||
|
|
||
|
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
File renamed without changes.
File renamed without changes.
File renamed without changes.
Binary file added
BIN
+136 KB
...ct-designs/effect-sizes-in-single-subject-designs_files/figure-html5/plot-1.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
File renamed without changes.
File renamed without changes.
File renamed without changes.
39 changes: 39 additions & 0 deletions
39
_posts/2020-10-15-clinical-implementation-discourse/clinical-implementation-discourse.Rmd
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| --- | ||
| title: "Simpler tools for clinical implementation of discourse analysis" | ||
| author: Rob Cavanaugh | ||
| date: 10-15-2020 | ||
| repository_url: https://github.com/rbcavanaugh/WIM_MATTR | ||
| output: | ||
| distill::distill_article: | ||
| self_contained: false | ||
| --- | ||
|
|
||
|
|
||
| ```{r setup, include=FALSE} | ||
| knitr::opts_chunk$set(echo = FALSE) | ||
| knitr::opts_chunk$set(collapse = TRUE) | ||
| knitr::opts_chunk$set(dev.args=list(bg="transparent")) | ||
| ``` | ||
|
|
||
|
|
||
| People with aphasia identify everyday communication as one of the highest priorities for treatment.^1^ One way of estimating everyday communication skills in clinical settings is discourse analysis. For example, people with aphasia are provided with one or more pictures and asked to describe the picture(s) or tell a story. However, analyzing these discourse samples requires time-intensive transcription, coding, and knowledge of software such as CLAN.^2^ For clinicians with limited time and ambitious productivity requirements, discourse analysis (as currently used by aphasia researchers) is not often feasible. In my experience in outpatient practice, 10 minutes scoring assessments (in addition to what I could score during the evaluation) while writing up an evaluation was a reasonable goal on a normal day. | ||
|
|
||
| One solution to this challenge is to minimize the time required for discourse analysis. For example, main concept analysis and core lexicon analysis^3^ are methods of measuring discourse without the need for precise transcription or discourse coding. Recently, Cunningham and Haley^4^ analyzed the use of two metrics for lexical diversity - a discourse measure that is thought to capture word retrieval skills in connected speech.^5^ What I find particularly interesting about these two metrics - the word information measure (WIM) and the moving average type-token ratio (MATTR) - is that they were analyzed using only orthographic transcriptions. Consequently, they don't require discourse coding required by CLAN. Instead they can be calculated using R. | ||
|
|
||
| Last spring, I worked on several projects where I built several interactive shiny apps^6^ using R so that our [team](https://lrcl.pitt.edu/) and collaborators could better understand complex data by interacting with it. As a proof of concept, last month I implemented the WIM and MATTR lexical diversity measures in a shiny app to demonstrate how straightforward it can be to provide a simple interface for these measures for clinicians to use. That proof-of-concept app is below ([and here](https://rb-cavanaugh.shinyapps.io/WIM_MATTR/)). The R code for the app is [here](https://github.com/rbcavanaugh/WIM_MATTR). Hopefully, it will be refined with instructions and norms as they come out. I also hope to incorporate an easy interface for main concept analysis and core lexicon in the future. | ||
|
|
||
| <iframe scrolling="no" src=" https://rb-cavanaugh.shinyapps.io/WIM_MATTR/" class="l-body-outset shaded" style="height: 100vh;"></iframe> | ||
|
|
||
| 1. Wallace SJ, Worrall L, Rose T, et al. Which outcomes are most important to people with aphasia and their families? an international nominal group technique study framed within the ICF. Disabil Rehabil. 2017;39(14):1364–1379. doi:10.1080/09638288.2016.1194899 | ||
|
|
||
| 2. MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk. Transcription format and programs (Vol. 1). Psychology Press. | ||
|
|
||
| 3. Dalton, S. G., & Richardson, J. D. (2015). Core-lexicon and main-concept production during picture-sequence description in adults without brain damage and adults with aphasia. American Journal of Speech-Language Pathology, 24(4), S923–S938. | ||
|
|
||
| 4. Cunningham, K. T., & Haley, K. L. (2020). Measuring Lexical Diversity for Discourse Analysis in Aphasia: Moving-Average Type–Token Ratio and Word Information Measure. Journal of Speech, Language, and Hearing Research, 63(3), 710-721. | ||
|
|
||
| 5. Fergadiotis, G., Wright, H. H., & West, T. M. (2013). Measuring lexical diversity in narrative discourse of people with aphasia. American Journal of Speech-Language Pathology. | ||
|
|
||
| 6. https://mastering-shiny.org/ | ||
|
|
||
|
|
Oops, something went wrong.