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<article class="article">
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<h2>tl;dr</h2>
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<p>People are busy, especially managers and leaders. Results of data analyses are sometimes presented in oral form, but often the first cut is presented via email.</p>
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<p>It is often useful therefore, to breakdown the results of an analysis into different levels of granularity/detail</p>
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<h2>Hierarchy of Information: Research Paper</h2>
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<ul>
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<li>Title / Author List
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<ul>
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<li>Speaks about what the paper is about</li>
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<li>Hopefully interesting</li>
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<li>No detail</li>
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</ul></li>
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<li>Abstract
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<ul>
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<li>Motivation of the problem</li>
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<li>Bottom Line Results</li>
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</ul></li>
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<li>Body / Results
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<ul>
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<li>Methods</li>
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<li>More detailed results</li>
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<li>Sensitivity Analysis</li>
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<li>Implication of Results</li>
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</ul></li>
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<li>Supplementary Materials / Gory Details
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<ul>
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<li>Details on what was done</li>
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</ul></li>
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<li>Code / Data / Really Gory Details
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<ul>
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<li>For reproducibility</li>
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</ul></li>
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</ul>
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<h2>Hierarchy of Information: Email Presentation</h2>
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<ul>
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<li>Subject Line / Subject Info
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<ul>
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<li>At a minimum: include one</li>
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<li>Can you summarize findings in one sentence?</li>
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</ul></li>
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<li>Email Body
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<ul>
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<li>A brief description of the problem / context: recall what was proposed and executed; summarize findings / results. (Total of 1-2 paragraphs)</li>
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<li>If action is needed to be taken as a result of this presentation, suggest some options and make them as concrete as possible</li>
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<li>If questions need to be addressed, try to make them yes / no</li>
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</ul></li>
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<li>Attachment(s)
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<ul>
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<li>R Markdown file</li>
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<li>knitr report</li>
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<li>Stay Concise: Don't spit out pages of code</li>
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</ul></li>
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<li>Links to Supplementary Materials
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<ul>
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<li>Code / Software / Data</li>
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<li>Github Repository / Project Website</li>
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</ul></li>
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</ul>
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<h2>DO: Start with Good Science</h2>
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<ul>
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<li>Remember: Garbage, in, garbage out</li>
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<li>Find a coherent focused question. This helps solve many problems</li>
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<li>Working with good collaborators reinforces good practices</li>
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<li>Something that's interesting to you will hopefully motivate good habits</li>
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</ul>
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<h2>DON'T: Do Things By Hand</h2>
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<ul>
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<li>Editing spreadsheets of data to "clean it up"
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<ul>
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<li>Removing outliers</li>
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<li>QA / QC</li>
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<li>Validating</li>
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</ul></li>
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<li>Editing tables or figures (e.g rounding, formatting)</li>
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<li>Downloading data from a website</li>
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<li>Moving data around your computer, splitting, or reformatting files.</li>
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</ul>
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<p>Things done by hand need to precisely documented (this is harder than it sounds!)</p>
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<h2>DON'T: Point and Click</h2>
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<ul>
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<li>Many data processing / statistical analysis packages have graphical user interfaces (GUIs)</li>
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<li>GUIs are convenient / intuitive but the actions you take with a GUI can be difficult for others to reproduce</li>
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<li>Some GUIs produce a log file or script which includes equivalent commands; these can be saved for later examination</li>
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<li>In general, be careful with data analysis software that is highly interactive; ease of use can sometimes lead to non-reproducible analyses.</li>
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<li>Other interactive software, such as text editors, are usually fine.</li>
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</ul>
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<h2>DO: Teach a Computer</h2>
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<p>If something needs to be done as part of your analysis / investigation, try to teach your computer to do it (even if you only need to do it once) </p>
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<p>In order to give your computer instructions, you need to write down exactly what you mean to do and how it should be done. Teaching a computer almost guarantees reproducibility</p>
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<p>For example, by, hand you can</p>
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<pre><code> 1. Go to the UCI Machine Learning Repository at http://archive.ics.uci.edu/mil/
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2. Download the Bike Sharing Dataset</code></pre>
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<p>Or you can teach your computer to do it using R</p>
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<pre><code class="language-R">download.file("http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip", "ProjectData/Bike-Sharing-Dataset.zip")</code></pre>
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<p>Notice here that:</p>
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<ul>
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<li>The full URL to the dataset file is specified</li>
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<li>The name of the file saved to your local computer is specified</li>
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<li>The directory to which the filed was saved is specified ("ProjectData")</li>
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<li>Code can always be executed in R (as long as link is available)</li>
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</ul>
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<h2>DO: Use Some Version Control</h2>
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<p>It helps you slow things down by adding changes into small chunks. (Don't just do one massive commit). It allows one to track / tag snapshots so that one can revert back to older versions of the project. Software like Github / Bitbucket / SourceForge make it easy to publish results.</p>
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<h2>DO: Keep Track of Your Software Environment</h2>
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<p>If you work on a complex project involving many tools / datasets, the software and computing environment can be critical for reproducing your analysis.</p>
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<p><strong>Computer Architecture</strong>: CPU (Intel, AMD, ARM), CPU Architecture, GPUs</p>
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<p><strong>Operating System</strong>: Windows, Mac OS, Linux / Unix</p>
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<p><strong>Software Toolchain</strong>: Compilers, interpreters, command shell, programming language (C, Perl, Python, etc.), database backends, data analysis software</p>
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<p><strong>Supporting software / infrastructure</strong>: Libraries, R packages, dependencies</p>
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<p><strong>External dependencies</strong>: Websites, data repositories, remote databases, software repositories</p>
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<p><strong>Version Numbers:</strong> Ideally, for everything (if available)</p>
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<p>This function in R helps report a bunch of information relating to the software environment</p>
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<pre><code class="language-R">sessionInfo()</code></pre>
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<h2>DON'T: Save Output</h2>
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<p>Avoid saving data analysis output (tables, figures, summaries, processed data, etc.), except perhaps temporarily for efficiency purposes.</p>
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<p>If a stray output file cannot be easily connected with the means by which it was created, then it is not reproducible</p>
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<p>Save the data + code that generated the output, rather than the output itself.</p>
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<p>Intermediate files are okay as long as there is clear documentation of how they were created.</p>
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<h2>DO: Set Your Seed</h2>
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<p>Random number generators generate pseudo-random numbers based on an initial seed (usually a number or set of numbers)</p>
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<p> In R, you can use the <code>set.seed()</code> function to set the seed and to specify the random number generator to use</p>
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<p>Setting the seed allows for the stream of random numbers to be exactly reproducible</p>
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<p>Whenever you generate random numbers for a non-trivial purpose, <strong>always set the seed</strong>.</p>
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<h2>DO: Think About the Entire Pipeline</h2>
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<ul>
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<li>Data analysis is a lengthy process; it is not just tables / figures/ reports</li>
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<li>Raw data -> processed data -> analysis -> report</li>
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<li>How you got the end is just as important as the end itself</li>
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<li>The more of the data analysis pipeline you can make reproducible, the better for everyone</li>
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</ul>
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<h2>Summary: Checklist</h2>
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<ul>
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<li>Are we doing good science?
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<ul>
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<li>Is this interesting or worth doing?</li>
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</ul></li>
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<li>Was any part of this analysis done by hand?
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<ul>
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<li>If so, are those parts precisely documented?</li>
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<li>Does the documentation match reality?</li>
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</ul></li>
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<li>Have we taught a computer to do as much as possible (i.e. coded)?</li>
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<li>Are we using a version control system?</li>
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<li>Have we documented our software environment?</li>
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<li>Have we saved any output that we cannot reconstruct from original data + code?</li>
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<li>How far back in the analysis pipeline can we go before our results are no longer (automatically reproducible)</li>
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</ul>
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<h2>Replication and Reproducibility</h2>
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<p>Replication</p>
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<ul>
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<li>Focuses on the validity of the scientific claim</li>
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<li>Is this claim true?</li>
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<li>The ultimate standard for strengtening scientiffic evidence</li>
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<li>New investigators, data, analytical methods, laboratories, instruments, etc.</li>
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<li>Particularly important in studies that can impact broad policy or regulatory decisions.</li>
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</ul>
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<p>Reproducibility</p>
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<ul>
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<li>Focuses on the validity of the data analysis</li>
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<li>Can we trust this analysis?</li>
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<li>Arguably a minimum standard for any scientific study</li>
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<li>New investigators, same data, same methods</li>
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<li>Important when replication is impossible</li>
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</ul>
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<h2>Background and Underlying Trends</h2>
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<ul>
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<li>Some studies cannot be replicated: No time, no money, or just plain unique / opportunistic</li>
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<li>Technology is increasing data collection throughput; data are more complex and high-dimensional</li>
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<li>Existing databases can be merged to become bigger databases (but data are used off-label)</li>
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<li>Computing power allows more sophisticated analyses, even on "small" data</li>
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<li>For every field "X", there is a "Computational X"</li>
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</ul>
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<h2>The Result?</h2>
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<ul>
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<li>Even basic analyses are difficult to describe</li>
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<li>Heavy computational requirements are thrust upon people without adequate training in statistics and computing</li>
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<li>Errors are more easily introduced into long analysis pipelines</li>
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<li>Knowledge transfer is inhibited</li>
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<li>Results are difficult to replicate or reproduce</li>
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<li>Complicated analyses cannot be trusted</li>
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</ul>
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<h2>What Problem Does Reproducibility Solve?</h2>
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<p>What we get:</p>
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<ul>
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<li>Transparency</li>
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<li>Data Availability</li>
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<li>Software / Methods of Availability</li>
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<li>Improved Transfer of Knowledge</li>
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</ul>
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<p>What we do NOT get</p>
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<ul>
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<li>Validity / Correctness of the analysis</li>
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</ul>
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<p>An analysis can be reproducible and still be wrong</p>
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<p>We want to know 'can we trust this analysis</p>
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<p>Does requiring reproducibility deter bad analysis?</p>
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<h2>Problems with Reproducibility</h2>
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<p>The premise of reproducible research is that with data/code available, people can check each other and the whole system is self-correcting</p>
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<ul>
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<li>Addresses the most "downstream" aspect of the research process -- Post-publication</li>
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<li>Assumes everyone plays by the same rules and wants to achieve the same goals (i.e. scientific discovery)</li>
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</ul>
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<h2>Who Reproduces Research?</h2>
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<ul>
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<li>For reproducibility to be effective as a means to check validity, someone needs to do something
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<ul>
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<li>Re-run the analysis; check results match</li>
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<li>Check the code for bugs/errors</li>
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<li>Try alternate approaches; check sensitivity</li>
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</ul></li>
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<li>The need for someone to do something is inherited from traditional notion of replication</li>
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<li>Who is "someone" and what are their goals?</li>
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</ul>
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<h2>The Story So Far</h2>
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<ul>
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<li>Reproducibility brings transparency (wrt code+data) and increased transfer of knowledge</li>
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<li>A lot of discussion about how to get people to share data</li>
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<li>Key question of "can we trust this analysis"? is not addressed by reproducibility</li>
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<li>Reproducibility addresses potential problems long after they've occurred ("downstream")</li>
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<li>Secondary analyses are inevitably colored by the interests/motivations of others.</li>
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</ul>
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<h2>Evidence-based Data Analysis</h2>
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<ul>
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<li>Most data analyses involve stringing together many different tools and methods</li>
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<li>Some methods may be standard for a given field, but others are often applied ad hoc</li>
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<li>We should apply throughly studied (via statistical research), mutually agreed upon methods to analyze data whenever possible</li>
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<li>There should be evidence to justify the application of a given method</li>
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</ul>
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<h2>Evidence-based Data Analysis</h2>
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<ul>
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<li>Create analytic pipelines from evidence-based components - standardize it</li>
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<li>A deterministic statistical machine</li>
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<li>Once an evidence-based analytic pipeline is established, we shouldn't mess with it</li>
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<li>Analysis with a "transparent box"</li>
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<li>Reduce the "research degrees of freedom"</li>
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<li>Analogous to a pre-specified clinical trial protocol</li>
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</ul>
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<h2>Case Study: Estimating Acute Effects of Ambient Air Pollution Exposure</h2>
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<ul>
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<li>Acute / Short-term effects typically estimated via panel studies or time series studies</li>
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<li>Work originated in late 1970s early 1980s</li>
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<li>Key question "Are short-term changes in pollution associated with short-term changes in a population health outcome?"</li>
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<li>Studies are usually conducted at a community level</li>
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<li>Long history of statistical research investigating proper methods of analysis</li>
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</ul>
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<h2>Case Study: Estimating Acute Effects of Ambient Air Pollution Exposure</h2>
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<ul>
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<li>Can we encode everything that we have found in statistical / epidemiological research into a single package?</li>
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<li>Time series studies do not have a huge range of variation; typically involves similar types of data and similar questions</li>
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<li>We can create a deterministic statistical machine for this area?</li>
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</ul>
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<h2>DSM Modules for Time Series Studies of Air Pollution and Health</h2>
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<ol>
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<li>Check for outliers, high leverage, overdispersion</li>
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<li>Fill in missing data? No!</li>
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<li>Model selection: Estimate degrees of freedom to adjust for unmeasured confounders
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<ul>
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<li>Other aspects of model not as critical</li>
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</ul></li>
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<li>Multiple lag analysis</li>
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<li>Sensitivity analysis wrt
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<ul>
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<li>Unmeasured confounder adjustment</li>
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<li>Influential points</li>
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</ul></li>
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</ol>
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<h2>Where to Go From Here?</h2>
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<ul>
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<li>One DSM is not enough, we need many!</li>
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<li>Different problems warrant different approaches and expertise</li>
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<li>A curated library of machines providing state-of-the-art analysis pipelines</li>
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<li>A CRAN/CPAN/CTAN/... for data analysis</li>
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<li>Or a "Cochrane Collaboration" for data analysis</li>
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</ul>
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<h2>A Curated Library of Data Analysis</h2>
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<ul>
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<li>Provide packages that encode data analysis pipelines for given problems, technologies, questions</li>
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<li>Curated by experts knowledgeable in the field</li>
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<li>Documentation / References given supporting module in the pipeline</li>
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<li>Changes introduced after passing relevant benchmarks/unit tests</li>
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</ul>
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<h2>Summary</h2>
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<ul>
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<li>Reproducible research is important, but does not necessarily solve the critical question of whether a data analysis is trustworthy</li>
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<li>Reproducible research focuses on the most "downstream" aspect of research documentation</li>
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<li>Evidence-based data analysis would provide standardized best practices for given scientific areas and questions</li>
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<li>Gives reviewers an important tool without dramatically increases the burden on them</li>
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<li>More effort should be put into improving the quality of "upstream" aspects of scientific research</li>
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</ul>
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