{"Package":"clinicalsignificance","Type":"Package","Title":"A Toolbox for Clinical Significance Analyses in Intervention\nStudies","Version":"2.1.0","Authors@R":"person(\"Benedikt\", \"Claus\", , \"b.claus@pedscience.de\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9501-0954\"))","Description":"A clinical significance analysis can be used to determine if an\nintervention has a meaningful or practical effect for patients. You provide\na tidy data set plus a few more metrics and this package will take care of\nit to make your results publication ready. Accompanying package to\nClaus et al. .","License":"GPL (>= 3)","Encoding":"UTF-8","LazyData":"true","Depends":"R (>= 2.10)","RoxygenNote":"7.3.2","Imports":"BayesFactor, bayestestR, cli, dplyr, ggplot2, insight, lme4,\npurrr, rlang, tibble, tidyr","Suggests":"knitr, rmarkdown, testthat (>= 3.0.0), tidyverse, vdiffr","Config/testthat/edition":"3","VignetteBuilder":"knitr","URL":"https://pedscience.github.io/clinicalsignificance/,\nhttps://github.com/pedscience/clinicalsignificance","BugReports":"https://github.com/pedscience/clinicalsignificance/issues","NeedsCompilation":"no","Packaged":"2024-12-02 14:58:09 UTC; bened","Author":"Benedikt Claus [aut, cre] ()","Maintainer":"Benedikt Claus ","Repository":"CRAN","Date/Publication":"2024-12-02 15:40:16 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/clinicalsignificance_2.1.0.tar.gz","jsonAuthors":[{"name":"Benedikt Claus","email":"b.claus@pedscience.de","maintainer":true}],"readme":"\n\n\n# clinicalsignificance $\"\"$ \n\n\n\n[![](https://www.r-pkg.org/badges/version/clinicalsignificance)](https://cran.r-project.org/package=clinicalsignificance)\n[![](http://cranlogs.r-pkg.org/badges/grand-total/clinicalsignificance)](https://cran.r-project.org/package=clinicalsignificance)\n\n[![R-CMD-check](https://github.com/pedscience/clinicalsignificance/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/pedscience/clinicalsignificance/actions/workflows/R-CMD-check.yaml)\n\n\nThe goal of this powerful package is to provide all necessary tools for\nanalyses of clinical significance in clinical intervention studies. In\ncontrast to *statistical* significance, which assesses if it is probable\nthat there is a treatment effect, *clinical* significance can be used to\ndetermine if a treatment effect is of practical use or meaningful for\npatients. This package is designed to help researchers and healthcare\nprofessionals determine the clinical relevance of their findings. It\nprovides various functions and tools for quantifying and visualizing\nclinical significance, making it easier to make informed decisions in\nthe medical field.\n\n## Installation\n\nYou can install the clinicalsignificance package from CRAN using the\nfollowing command:\n\n``` r\ninstall.packages(\"clinicalsignificance\")\n```\n\nAlternatively, you can install the development version from GitHub:\n\n``` r\ndevtools::install_github(\"pedscience/clinicalsignificance\")\n```\n\n## Main Functions\n\nThe main functions of the package are based on the main approaches for\nclinical significance analyses, i.e., the anchor-based approach, the\npercentage-change approach, the distribution-based approach, the\nstatistical, and combined approaches. Hence, the main functions are\n\n- `cs_anchor()`\n- `cs_percentage()`\n- `cs_distribution()`\n- `cs_statistical()`\n- `cs_combined()`\n\nSee the package vignettes for additional details on the various clinical\nsignificance approaches.\n\n## Example\n\nClaus et al. (2020) implemented a novel intervention to enhance the\neffectiveness of antidepressants by boosting the inherent placebo effect\nof that medication. The randomized patients to treatment as usual (TAU)\nand a placebo amplification (PA) group and measured the severity of\ndepressive symptoms over time.\n\nIn the anchor-based approach, a clinical significant change is believed\nto have occured if a given change is greater or equal to the minimally\nimportant difference of the used instrument. Given a tidy dataset of the\nstudy and a minimally important difference for the Beck Depression\nInventory (second edition, BDI-II) of 7 points, the study data by Claus\net al. (2020) may be analyzed as follows:\n\n``` r\nlibrary(clinicalsignificance)\n\ncs_results <- claus_2020 |>\n cs_anchor(\n id = id,\n time = time,\n outcome = bdi,\n pre = 1,\n post = 4,\n mid_improvement = 7\n )\n\ncs_results\n#> \n#> ── Clinical Significance Results ──\n#> \n#> Individual anchor-based approach with a 7 point decrease in instrument scores\n#> indicating a clinical significant improvement.\n#> Category | n | Percent\n#> ---------------------------\n#> Improved | 25 | 62.50%\n#> Unchanged | 11 | 27.50%\n#> Deteriorated | 4 | 10.00%\n```\n\nYou can receive a detailed summary of the analysis by\n\n``` r\nsummary(cs_results)\n#> \n#> ── Clinical Significance Results ──\n#> \n#> Individual anchor-based analysis of clinical significance with a 7 point\n#> decrease in instrument scores (bdi) indicating a clinical significant\n#> improvement.\n#> There were 43 participants in the whole dataset of which 40 (93%) could be\n#> included in the analysis.\n#> \n#> ── Individual Level Results\n#> Category | n | Percent\n#> ---------------------------\n#> Improved | 25 | 62.50%\n#> Unchanged | 11 | 27.50%\n#> Deteriorated | 4 | 10.00%\n```\n\nor plot the cs_results with\n\n``` r\nplot(cs_results)\n```\n\n

\n\n### Including Groups\n\nGroup-wise results may be obtained by setting the `group` argument\n\n``` r\ncs_results_grouped <- claus_2020 |>\n cs_anchor(\n id = id,\n time = time,\n outcome = bdi,\n pre = 1,\n post = 4,\n mid_improvement = 7,\n group = treatment\n )\n\nsummary(cs_results_grouped)\n#> \n#> ── Clinical Significance Results ──\n#> \n#> Individual anchor-based analysis of clinical significance with a 7 point\n#> decrease in instrument scores (bdi) indicating a clinical significant\n#> improvement.\n#> There were 43 participants in the whole dataset of which 40 (93%) could be\n#> included in the analysis.\n#> \n#> ── Individual Level Results\n#> Group | Category | n | Percent\n#> -----------------------------------\n#> TAU | Improved | 8 | 20.00%\n#> TAU | Unchanged | 7 | 17.50%\n#> TAU | Deteriorated | 4 | 10.00%\n#> PA | Improved | 17 | 42.50%\n#> PA | Unchanged | 4 | 10.00%\n#> PA | Deteriorated | 0 | 0.00%\nplot(cs_results_grouped)\n```\n\n

\n\n### Clinical Significance Categories\n\nThe individual categories may be visualized by adjusting the argument\n`show` in the `plot()` function call.\n\n``` r\nplot(cs_results, show = category)\n```\n\n

\n\n## Contributing\n\nWe welcome contributions from the R community to enhance the package. If\nyou find any bugs, have feature requests, or would like to contribute\nimprovements, please open an issue or submit a pull request on GitHub.\n\n## License\n\nThis package is released under the GNU General Public License. You are\nfree to use and distribute it according to the terms of the license.\n\n------------------------------------------------------------------------\n\nThank you for using the clinicalsignificance R package! We hope it\nproves to be a valuable tool for assessing clinical significance in your\nmedical and healthcare research. If you find it helpful, consider giving\nus a star on GitHub and spreading the word.","jobInfo":{"package":"clinicalsignificance","version":"2.1.0","parsingStatus":"success","parserVersion":2,"parsedAt":"2024-12-02T17:29:56+0000"}}

Description

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