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<h2 class="hd hd-2 unit-title">Introduction</h2>
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<p><span lang="EN-GB">Welcome to the Tools for Data Science Workshop. </span></p>
<p><span lang="EN-GB">This workshop further explores the important concept of reproducibility in critical care research. It starts with a brief introduction to the topic, followed by an exercise using the MIMIC-III database. A video will guide you through the exercise.<o:p></o:p></span></p>
<p><span lang="EN-GB">At the end you will have the chance to perform a simple query.</span></p>
<p></p>
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<p>1. Understand the concept of reproducibility in critical care research.</p>
<p>2. Get to know different tools that facilitate reproducibility.</p>
<p>3. Understand how to use RMarkdown in order to create a reproducible analysis.</p>
<p>4. Learn how to perform a simple query.</p>
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<p>Workshop Author:<span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;"> Tom Pollard</span></p>
<p><span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;">Video: Videos in this unit are presented by </span><span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;">Joshua K. Hartshorne and Tom Pollard.</span></p>
<p><span style="color: #313131; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 1em;">Adaptation: Amelie Mathusek, Sreekar Mantena</span></p>
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<h2 class="hd hd-2 unit-title">Reproducibility in Critical Care - Theoretical Concepts</h2>
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<p><span lang="EN-GB">The reproducibility crisis is an issue facing scientific researchers around the world, where the results of studies are not able to be replicated. In recent meta-analyses, experts have found that between 40-80% of studies in psychology, cardiology, hematology, and more were not reproducible.</span></p>
<p><span lang="EN-GB">Nowadays, studies analyze hundreds and thousands of datapoints, and data analysis is becoming infinitely more complex. Reproducibility is particularly important in bioinformatics, since results from these analyses are used to guide patient treatment. </span></p>
<p></p>
<p>Reproducibility is generally divided into two major groups:</p>
<ul>
<li>Conceptual Replication: You are asking the same question, but doing so in a different way than originally done. This tests the validity of the underlying theory.</li>
<li>Exact Replication: You are asking the exact same question in the same way as was originally done. This tests the validity of theoretical statistical conclusions.</li>
</ul>
<p>Failing to achieve exact replication is particularly concerning, because it calls into question the validity of studies previously published in the literature.</p>
<p></p>
<p>There are many factors currently contributing to the reproducibility crisis, including:</p>
<ul>
<li>
<p><span lang="EN-GB">Pressure to publish: Scientists may perform statistical tests which depict their results in the most favorable way for publication.<o:p></o:p></span></p>
</li>
<li>
<p><span lang="EN-GB">Selective reporting: Oftentimes, only the most positive outcomes or analyses are reported in the literature and all the data is not shared.<o:p></o:p></span></p>
</li>
<li>
<p><span style="font-family: Symbol;" lang="EN-GB"><span style="font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><span lang="EN-GB">Insufficient mentoring: In academic settings, students without proper mentorship may interpret or analyze data incorrectly, resulting in results that are not able to be reproduced.<o:p></o:p></span></p>
</li>
<li>
<p><span lang="EN-GB">Methods - codes not available: Reviewers and scientists are often unable to verify the replicability of a study because the authors do not make the code public.</span></p>
</li>
<li>
<p><span style="font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span><span lang="EN-GB">Insufficient peer review: Many peer-reviewed journals do not go through the author's code, which can lead to incorrect analyses being published.</span></p>
</li>
</ul>
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<p style="text-rendering: optimizelegibility; margin-right: 0px; margin-left: 0px; padding: 0px; border: 0px; outline: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-stretch: inherit; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 16px; vertical-align: baseline; color: #313131;"><span color="#313131" face="Open Sans, Helvetica Neue, Helvetica, Arial, sans-serif" style="text-rendering: optimizelegibility; margin: 0px; padding: 0px; border: 0px; outline: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: 1.4em; vertical-align: baseline;">Joshua K. Hartshorne<br style="text-rendering: optimizelegibility; line-height: 1.4em;" /></span>Assistant Professor of Psychology<br style="text-rendering: optimizelegibility; line-height: 1.4em;" />Boston College</p>
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<h3 class="hd hd-2">Defining Replication</h3>
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<h2 class="hd hd-2 unit-title">Best-Practices to Ensure Reproducibility</h2>
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<p>There are many steps we can take as students, researchers, and scientists to perform studies and analyses which are fully-reproducible.</p>
<p>Most notably, we can:</p>
<ol>
<li>Consider the power of statistical tests and their weaknesses.</li>
<li>Use prior knowledge to perform 'sanity' checks of your work along the way to avoid reaching incorrect conclusions.</li>
<li>Be careful to use statistical inference and hypothesis testing correctly, understand their assumptions, and only employ them when the assumptions are met.</li>
<li>Stay current by using modern, field-accepted analysis methodologies.</li>
<li>Think carefully about correcting for multiple comparisons in large studies.</li>
<li>Write a code that is highly-commented to explain the reasoning behind the analyses done.</li>
<li>Release the code for the public when publishing a study so peers can follow all steps of your data analysis. (Few scientific studies today are published with the code freely available for others to use, and hopefully this trend will change in the coming years).</li>
</ol>
<p></p>
<p>There are a plethora of tools available to help you increase the reproducibility of your code and enable others to understand what you have done.</p>
<p>These tools include:</p>
<p>1. Executable notebooks such as RMarkdown </p>
<p>2. Code publishing platforms such as GitHub</p>
<p>3. Version Control Systems that integrate with GitHub</p>
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<h2 class="hd hd-2 unit-title">Installing R and RStudio</h2>
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<p style="font-size: 16px;"><span lang="EN-US">We will begin by downloading RStudio, an </span><span lang="EN-US"><span class="st">integrated development environment</span> (IDE) that enables us to quickly write R code and view our results.<br /> </span></p>
<p><span lang="EN-US">1. Download R using <a href="https://cran.rstudio.com" target="[object Object]">this link</a> if you do not have R installed on your computer already.</span></p>
<p><span lang="EN-US">2. </span><span lang="EN-US">Download RStudio <a href="https://rstudio.com/products/rstudio/download/#download" target="[object Object]">here</a>. You will need to have R installed as well in order for RStudio to function.</span></p>
<p><span lang="EN-US">3. Now d</span><span lang="EN-US" style="font-size: 16px;">ownload <a href="/assets/courseware/v1/c677d6329d6fcb7348eac554c7d2118f/asset-v1:MITx+HST.953x+3T2020+type@asset+block/workshop_tools_for_data_science_HST.953x.Rmd" target="[object Object]">the workshop file</a> and open it in R Studio.</span></p>
<p> </p>
<p>If you want to learn more about R, feel free to take other edX courses like <a href="https://www.edx.org/course/data-science-r-basics" target="[object Object]">this one</a> to get more familiar with this programming language.</p>
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<h2 class="hd hd-2 unit-title">Case Study: Reproducing Studies Using MIMIC-III</h2>
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<p>Now that you have installed R itself and RStudio, the video will guide you through the next steps and explain how to run the code. It also includes a brief tutorial on extracting patient length-of-stay data from MIMIC-III.</p>
<p><strong>IMPORTANT:</strong></p>
<ul>
<li>In order for you to complete this exercise, you need to create a <a href="https://console.cloud.google.com/bigquery" target="[object Object]">Google BigQuery project</a> first.</li>
<li>Then, change the project ID<code> </code>to your own BigQuery project ID. The project ID "hst-953-2018", also mentioned in the video below, has expired.</li>
</ul>
<p><img height="171" width="862" src="/assets/courseware/v1/fd21fd1fc1ad2e7e2a6df0d4c1893da1/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Bildschirmfoto_2020-08-29_um_14.38.09.png" alt="" /></p>
<ul>
<li>In case you encounter the error "Error: Can't get Google credentials.", please copy "<code>bigrquery::bq_auth()"</code> into the R console. See screenshot below.</li>
</ul>
<p><img height="0" width="0" src="/assets/courseware/v1/e59863df6440580947f260b560f7b9e9/asset-v1:MITx+HST.953x+3T2020+type@asset+block/screenshot.tiff" alt="" /><img height="271" width="890" src="/assets/courseware/v1/4b8975021b432a4368ab6800105bf89f/asset-v1:MITx+HST.953x+3T2020+type@asset+block/screenshot.png" alt="" /></p>
<p></p>
<p><strong>Helpful things to consider:</strong></p>
<ul>
<li>In case you discover any problems when running the code you might have to install/update additional packages such as “reader”, “ggplot2” (version 3.3.2) and “bigrquery” (version 1.3.1). You will most likely be asked to do so by RStudio.</li>
<li>This exercise has been created by R versions 1.2 and 1.3. Older or more recent versions might use different code expressions so the code would need to be modified to run smoothly.</li>
</ul>
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<h3 class="hd hd-2">Case Study: Reproducing Studies Using MIMIC-III</h3>
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