Mentioning the HADES paper on the home page. Adding some more publica…

…tions
OHDSI · Jan 30, 2024 · 712ad9e · 712ad9e
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diff --git a/Rmd/hideTitle.css b/Rmd/hideTitle.css
@@ -1,3 +1,11 @@
 .title {
 	display: none;
 }
+
+.citation {
+  display: block;
+  overflow:hidden; 
+  padding: 0.5em;
+  margin-bottom: 10px;
+  background: #f5f5f5;
+}
diff --git a/Rmd/index.Rmd b/Rmd/index.Rmd
@@ -10,7 +10,7 @@ output:
 
 HADES (formally known as the OHDSI Methods Library) is a set of open source R packages for large scale analytics, including population characterization, population-level causal effect estimation, and patient-level prediction.
 
-The packages offer R functions that together can be used to perform an observation study from data to estimates and supporting statistics, figures, and tables. The packages interact directly with observational data in the [Common Data Model (CDM)](https://ohdsi.github.io/CommonDataModel/), and are designed to support both large datasets and large numbers of analyses (e.g. for testing many hypotheses including control hypotheses, and testing many analyses design variations). For this purpose, each Method package includes functions for specifying and subsequently executing multiple analyses efficiently. HADES supports best practices for use of observational data as learned from previous and ongoing research, such as transparency, reproducibility, as well as measuring of the operating characteristics of methods in a particular context and subsequent empirical calibration of estimates produced by the methods.
+The packages offer R functions that together can be used to perform an observation study from data to estimates and supporting statistics, figures, and tables. The packages interact directly with observational data in the [Common Data Model (CDM)](https://ohdsi.github.io/CommonDataModel/), and are designed to support both large datasets and large numbers of analyses (e.g. for testing many hypotheses including control hypotheses, and testing many analyses design variations). For this purpose, each Method package includes functions for specifying and subsequently executing multiple analyses efficiently. HADES supports best practices for use of observational data as learned from previous and ongoing research, such as transparency, reproducibility, as well as measuring of the operating characteristics of methods in a particular context and subsequent empirical calibration of estimates produced by the methods. For more information about HADES' design considerations, please refer to the [HADES paper](https://ebooks.iospress.nl/doi/10.3233/SHTI231108).
 
 HADES has already been used in many published clinical and methodological studies, as can be seen in the [Publications section](publications.html).
 
@@ -22,6 +22,14 @@ See the Support section for instructions on [setting up the R environment](rSetu
 
 <a href="http://book.ohdsi.org"><img src="images/book.png" width="250" height="400" alt="Cover image" align="right" style="margin: 0 1em 0 1em" /></a> Learn how to use HADES to produce reliable evidence from real-world data with The Book of OHDSI. Read it <a href="http://book.ohdsi.org">online</a>.
 
+# Citing HADES
+
+Please cite our [HADES paper](https://ebooks.iospress.nl/doi/10.3233/SHTI231108) when using any of the HADES packages in your work:
+
+```{block2, type='citation'}
+Schuemie M, Reps J, Black A, Defalco F, Evans L, Fridgeirsson E, Gilbert JP, Knoll C, Lavallee M, Rao GA, Rijnbeek P, Sadowski K, Sena A, Swerdel J, Williams RD, Suchard M. *Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.* Stud Health Technol Inform. 2024 Jan 25;310:966-970. doi: 10.3233/SHTI231108.
+```
+
 ## Technology
 
 HADES is a set of R packages that execute against data in a database server. HADES supports traditional database systems (PostgreSQL, Microsoft SQL Server, and Oracle), parallel data warehouses (e.g. Amazon RedShift), as well as 'Big Data' platforms (e.g. Google BigQuery). HADES does *not* support MySQL. The full list of supported database platforms can be found [here](supportedPlatforms.html).

diff --git a/Rmd/publications.Rmd b/Rmd/publications.Rmd
@@ -9,9 +9,9 @@ These papers were written using HADES.
 
 # Clinical application
 
-- Voss EA, Shoaibi A, Yin Hui Lai L, et al. [Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study](https://doi.org/10.1016/j.eclinm.2023.101932) EClinicalMedicine. 2023 Apr;58:101932. 
-
+- Khera R, Dhingra LS, Aminorroaya A, et al. [Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM](https://pubmed.ncbi.nlm.nih.gov/37829182/) BMJ Med. 2023 Oct 6;2(1):e000651. 
 
+- Voss EA, Shoaibi A, Yin Hui Lai L, et al. [Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study](https://doi.org/10.1016/j.eclinm.2023.101932) EClinicalMedicine. 2023 Apr;58:101932. 
 
 - Chandran U, Reps J, Yang R, et al. [Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care](https://doi.org/10.1158/1055-9965.epi-22-0873) Cancer Epidemiol Biomarkers Prev. 2023 Mar 6;32(3):337-343. 
 
@@ -98,6 +98,14 @@ These papers were written using HADES.
 
 # Methods research
 
+- Schuemie M, Reps J, Black A, et al. [Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research](https://pubmed.ncbi.nlm.nih.gov/38269952/) Stud Health Technol Inform. 2024 Jan 25:310:966-970. 
+
+- Bu F, Schuemie MJ, Nishimura A, et al. [Bayesian safety surveillance with adaptive bias correction](https://pubmed.ncbi.nlm.nih.gov/38010062/) Stat Med. 2024 Jan 30;43(2):395-418.
+
+- Voss EA, Blacketer C, van Sandijk S, et al. [European Health Data & Evidence Network-learnings from building out a standardized international health data network](https://pubmed.ncbi.nlm.nih.gov/37952118/) J Am Med Inform Assoc. 2023 Dec 22;31(1):209-219.
+
+- Gauffin O, Brand JS, Vidlin SH, et al. [Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study](https://pubmed.ncbi.nlm.nih.gov/37804398/) Drug Saf. 2023 Dec;46(12):1335-1352. 
+
 - Wu Q, Schuemie MJ, Suchard MA, et al. [Pade approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes](https://doi.org/10.1016/j.jbi.2023.104476) J Biomed Inform. 2023 Aug 18:104476. 
 
 - Arshad F, Schuemie MJ, Bu F, et al. [Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance](https://doi.org/10.1007/s40264-023-01324-1) Drug Saf. 2023 Aug;46(8):797-807. 

diff --git a/docs/hideTitle.css b/docs/hideTitle.css
@@ -1,3 +1,11 @@
 .title {
 	display: none;
 }
+
+.citation {
+  display: block;
+  overflow:hidden; 
+  padding: 0.5em;
+  margin-bottom: 10px;
+  background: #f5f5f5;
+}
diff --git a/docs/index.html b/docs/index.html
@@ -459,7 +459,10 @@ <h1 class="title toc-ignore">HADES</h1>
 from previous and ongoing research, such as transparency,
 reproducibility, as well as measuring of the operating characteristics
 of methods in a particular context and subsequent empirical calibration
-of estimates produced by the methods.</p>
+of estimates produced by the methods. For more information about HADES’
+design considerations, please refer to the <a
+href="https://ebooks.iospress.nl/doi/10.3233/SHTI231108">HADES
+paper</a>.</p>
 <p>HADES has already been used in many published clinical and
 methodological studies, as can be seen in the <a
 href="publications.html">Publications section</a>.</p>
@@ -479,6 +482,20 @@ <h1>Learn How to Use HADES</h1>
 Learn how to use HADES to produce reliable evidence from real-world data
 with The Book of OHDSI. Read it
 <a href="http://book.ohdsi.org">online</a>.</p>
+</div>
+<div id="citing-hades" class="section level1">
+<h1>Citing HADES</h1>
+<p>Please cite our <a
+href="https://ebooks.iospress.nl/doi/10.3233/SHTI231108">HADES paper</a>
+when using any of the HADES packages in your work:</p>
+
+<div class="citation">
+Schuemie M, Reps J, Black A, Defalco F, Evans L, Fridgeirsson E, Gilbert
+JP, Knoll C, Lavallee M, Rao GA, Rijnbeek P, Sadowski K, Sena A, Swerdel
+J, Williams RD, Suchard M. <em>Health-Analytics Data to Evidence Suite
+(HADES): Open-Source Software for Observational Research.</em> Stud
+Health Technol Inform. 2024 Jan 25;310:966-970. doi: 10.3233/SHTI231108.
+</div>
 <div id="technology" class="section level2">
 <h2>Technology</h2>
 <p>HADES is a set of R packages that execute against data in a database

diff --git a/docs/packageStatuses.html b/docs/packageStatuses.html
@@ -13,7 +13,7 @@
 
 <title>Package statuses</title>
 
-<script src="site_libs/header-attrs-2.24/header-attrs.js"></script>
+<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
 <script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 <link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />

diff --git a/docs/publications.html b/docs/publications.html
@@ -445,6 +445,11 @@ <h1 class="title toc-ignore">Publications</h1>
 <div id="clinical-application" class="section level1">
 <h1>Clinical application</h1>
 <ul>
+<li><p>Khera R, Dhingra LS, Aminorroaya A, et al. <a
+href="https://pubmed.ncbi.nlm.nih.gov/37829182/">Multinational patterns
+of second line antihyperglycaemic drug initiation across cardiovascular
+risk groups: federated pharmacoepidemiological evaluation in
+LEGEND-T2DM</a> BMJ Med. 2023 Oct 6;2(1):e000651.</p></li>
 <li><p>Voss EA, Shoaibi A, Yin Hui Lai L, et al. <a
 href="https://doi.org/10.1016/j.eclinm.2023.101932">Contextualising
 adverse events of special interest to characterise the baseline
@@ -669,6 +674,25 @@ <h1>Clinical application</h1>
 <div id="methods-research" class="section level1">
 <h1>Methods research</h1>
 <ul>
+<li><p>Schuemie M, Reps J, Black A, et al. <a
+href="https://pubmed.ncbi.nlm.nih.gov/38269952/">Health-Analytics Data
+to Evidence Suite (HADES): Open-Source Software for Observational
+Research</a> Stud Health Technol Inform. 2024 Jan
+25:310:966-970.</p></li>
+<li><p>Bu F, Schuemie MJ, Nishimura A, et al. <a
+href="https://pubmed.ncbi.nlm.nih.gov/38010062/">Bayesian safety
+surveillance with adaptive bias correction</a> Stat Med. 2024 Jan
+30;43(2):395-418.</p></li>
+<li><p>Voss EA, Blacketer C, van Sandijk S, et al. <a
+href="https://pubmed.ncbi.nlm.nih.gov/37952118/">European Health Data
+&amp; Evidence Network-learnings from building out a standardized
+international health data network</a> J Am Med Inform Assoc. 2023 Dec
+22;31(1):209-219.</p></li>
+<li><p>Gauffin O, Brand JS, Vidlin SH, et al. <a
+href="https://pubmed.ncbi.nlm.nih.gov/37804398/">Supporting
+Pharmacovigilance Signal Validation and Prioritization with Analyses of
+Routinely Collected Health Data: Lessons Learned from an EHDEN Network
+Study</a> Drug Saf. 2023 Dec;46(12):1335-1352.</p></li>
 <li><p>Wu Q, Schuemie MJ, Suchard MA, et al. <a
 href="https://doi.org/10.1016/j.jbi.2023.104476">Pade approximant meets
 federated learning: A nearly lossless, one-shot algorithm for evidence