From 402548dd70ce91b7d59c29d2b9109a7803899e40 Mon Sep 17 00:00:00 2001 From: egillax Date: Mon, 28 Oct 2024 09:42:20 +0100 Subject: [PATCH] fix file extensions --- vignettes/BestPractices.Rmd | 291 +++++++++++++++++++++++++++++++++++ vignettes/ClinicalModels.Rmd | 46 ++++++ vignettes/Videos.Rmd | 67 ++++++++ 3 files changed, 404 insertions(+) create mode 100644 vignettes/BestPractices.Rmd create mode 100644 vignettes/ClinicalModels.Rmd create mode 100644 vignettes/Videos.Rmd diff --git a/vignettes/BestPractices.Rmd b/vignettes/BestPractices.Rmd new file mode 100644 index 000000000..1fba21806 --- /dev/null +++ b/vignettes/BestPractices.Rmd @@ -0,0 +1,291 @@ +--- +title: "Best Practice Research" +author: "Jenna Reps, Peter R. Rijnbeek" +date: '`r Sys.Date()`' +header-includes: + - \usepackage{fancyhdr} + - \pagestyle{fancy} + - \fancyhead{} + - \fancyhead[CO,CE]{Installation Guide} + - \fancyfoot[CO,CE]{PatientLevelPrediction Package Version `r utils::packageVersion("PatientLevelPrediction")`} + - \fancyfoot[LE,RO]{\thepage} + - \renewcommand{\headrulewidth}{0.4pt} + - \renewcommand{\footrulewidth}{0.4pt} +output: + pdf_document: + includes: + in_header: preamble.tex + number_sections: yes + toc: yes + word_document: + toc: yes + html_document: + number_sections: yes + toc: yes +--- + +```{=html} + +``` +## Best practice publications using the OHDSI PatientLevelPrediction framework + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Topic + +Research Summary + +Link +
+Problem Specification + +When is prediction suitable in observational data? + +Guidelines needed +
+Data Creation + +Comparison of cohort vs case-control design + +Journal of Big Data +
+Data Creation + +Addressing loss to follow-up (right censoring) + +BMC medical informatics and decision makingk +
+Data Creation + +Investigating how to address left censoring in features construction + +BMC Medical Research Methodology +
+Data Creation + +Impact of over/under-sampling + + Journal of big data +
+Data Creation + +Impact of phenotypes + +Study Done - Paper submitted +
+Model development + +How much data do we need for prediction - Learning curves at scale + +International Journal of Medical Informatics +
+Model development + +What impact does test/train/validation design have on model performance + +BMJ Open +
+Model development + +What is the impact of the classifier + +JAMIA +
+Model development + +Can we find hyper-parameter combinations per classifier that consistently lead to good performing models when using claims/EHR data? + +Study needs to be done +
+Model development + +Can we use ensembles to combine different algorithm models within a database to improve models transportability? + + Caring is Sharing–Exploiting the Value in Data for Health and Innovation +
+Model development + +Can we use ensembles to combine models developed using different databases to improve models transportability? + + BMC Medical Informatics and Decision Making +
+Model development + +Impact of regularization method + + JAMIA +
+Evaluation + +Why prediction is not suitable for risk factor identification + + Machine Learning for Healthcare Conference +
+Evaluation + +Iterative pairwise external validation to put validation into context + + Drug Safety +
+Evaluation + +A novel method to estimate external validation using aggregate statistics + + Study under review +
+Evaluation + +How should we present model performance? (e.g., new visualizations) + +JAMIA Open +
+Evaluation + +How to interpret external validation performance (can we figure out why the performance drops or stays consistent)? + +Study needs to be done +
+Evaluation + +Recalibration methods + +Study needs to be done +
+Evaluation + +Is there a way to automatically simplify models? + +Study protocol under development +
+ diff --git a/vignettes/ClinicalModels.Rmd b/vignettes/ClinicalModels.Rmd new file mode 100644 index 000000000..3b6a5e5ae --- /dev/null +++ b/vignettes/ClinicalModels.Rmd @@ -0,0 +1,46 @@ +--- +title: "Clinical Models" +author: "Jenna Reps, Peter R. Rijnbeek" +date: '`r Sys.Date()`' +header-includes: + - \usepackage{fancyhdr} + - \pagestyle{fancy} + - \fancyhead{} + - \fancyhead[CO,CE]{Installation Guide} + - \fancyfoot[CO,CE]{PatientLevelPrediction Package Version `r utils::packageVersion("PatientLevelPrediction")`} + - \fancyfoot[LE,RO]{\thepage} + - \renewcommand{\headrulewidth}{0.4pt} + - \renewcommand{\footrulewidth}{0.4pt} +output: + pdf_document: + includes: + in_header: preamble.tex + number_sections: yes + toc: yes + word_document: + toc: yes + html_document: + number_sections: yes + toc: yes +--- + +```{=html} + +``` + +## Clinical models developed using the OHDSI PatientLevelPrediction framework + +| Title | Link | +|----------------------|-------| +| Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery | [Value in Health](https://www.sciencedirect.com/science/article/pii/S1098301519300737) | +| Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network | [PLoS One](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226718) | +| Wisdom of the CROUD: development and validation of a patient-level prediction model for opioid use disorder using population-level claims data | [PLoS One](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228632) | +| Developing predictive models to determine Patients in End-of-life Care in Administrative datasets | [Drug Safety](https://link.springer.com/article/10.1007/s40264-020-00906-7) | +| Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study | [Translational psychiatry](https://www.nature.com/articles/s41398-021-01760-6) | +| Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network | [BMC Medical Research Methodology](https://link.springer.com/article/10.1186/s12874-022-01505-z) | +| 90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model | [Knee Surgery, Sports Traumatology, Arthroscopy](https://link.springer.com/article/10.1007/s00167-021-06799-y) | +| Machine learning and real-world data to predict lung cancer risk in routine care | [Cancer Epidemiology, Biomarkers & Prevention](https://aacrjournals.org/cebp/article-abstract/32/3/337/718495) | +| Development and validation of a patient-level model to predict dementia across a network of observational databases | [BMC medicine](https://link.springer.com/article/10.1186/s12916-024-03530-9) | \ No newline at end of file diff --git a/vignettes/Videos.Rmd b/vignettes/Videos.Rmd new file mode 100644 index 000000000..5f278c92b --- /dev/null +++ b/vignettes/Videos.Rmd @@ -0,0 +1,67 @@ +--- +title: "Demo Videos" +author: "Jenna Reps, Peter R. Rijnbeek" +date: '`r Sys.Date()`' +header-includes: + - \usepackage{fancyhdr} + - \pagestyle{fancy} + - \fancyhead{} + - \fancyhead[CO,CE]{Installation Guide} + - \fancyfoot[CO,CE]{PatientLevelPrediction Package Version `r utils::packageVersion("PatientLevelPrediction")`} + - \fancyfoot[LE,RO]{\thepage} + - \renewcommand{\headrulewidth}{0.4pt} + - \renewcommand{\footrulewidth}{0.4pt} +output: + pdf_document: + includes: + in_header: preamble.tex + number_sections: yes + toc: yes + word_document: + toc: yes + html_document: + number_sections: yes + toc: yes +--- + +```{=html} + +``` +## What is a cohort table? + +| Click To Launch | Description of Demo | +|-----------------------------------------------|------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/BEukCbT8UoA/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/GY2ZTcizY90) | Learn what a cohort table looks like and what columns are required. | + +## Setting up a connection between your database and R + +| Click To Launch | Description of Demo | +|----------------------------------------|--------------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/BEukCbT8UoA/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/8F2X5SKN64w) | Learn how to configure the connection to your OMOP CDM data from R using the OHDSI DatabaseConnector package. | + +## Running a single PatientLevelPrediction model + +| Click To Launch | Description of Demo | +|-----------------------------------------------|-------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/7AraOsTynD4/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/7AraOsTynD4) | Learn how to develop and validate a single PatientLevelPrediction model. | + +## Running multiple PatientLevelPrediction models study + +| Click To Launch | Description of Demo | +|-----------------------------------------------|-------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/7wUilx580PE/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/7wUilx580PE) | Learn how to develop and validate multiple PatientLevelPrediction models. | + +## Exploring the results in the shiny app + +| Click To Launch | Description of Demo | +|---------------------------------------|---------------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/BulmuH32y_Y/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/BulmuH32y_Y) | Learn how to interactively explore the model performance and model via the shiny apps viewPlp() and viewMultiplePlp() | + +## Validating existing models on OMOP CDM data + +| Click To Launch | Description of Demo | +|--------------------------|----------------------------------------------| +| [![Video Vignette PLP Package](http://img.youtube.com/vi/BEukCbT8UoA/0.jpg){alt="Video Vignette PLP Package"}](https://youtu.be/oBsfg9hfrpI) | This demo shows how you can add any existing score or logistic model and validate the model on new OMOP CDM data. This is useful for benchmarking when developing new models or to perform extensive external validation of a model across the OHDSI network. |