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research.html
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<h2>Research</h2>
<p>The general question that I address in my research is:</p>
<ul style="list-style-type:none;">
<li><i>How should one report inferences about a population parameter when it is not estimable from observable data without the imposition of strong, untestable assumptions?</i></li>
</ul>
<p> This question is relevant to a wide variety of settings in prospective randomized and observational studies. For example, how should one evaluate the causal effects of treatment </p>
<ul style="list-style-type:disc;">
<li>in studies with potentially informative missing or censored data;</li>
<li>on functional outcomes in studies with high mortality rates;</li>
<li>in studies with non-compliance;</li>
<li>in studies with uncontrolled treatment assignment.</li>
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<p>In my research, I focus on frequentist and Bayesian approaches to evaluating the robustness of results to assumptions such as missing at random and no unmeasured confounding. I am interested in formal sensitivity analysis procedures as well as methods for constructing bounds on treatment effects. I favor the incorporation of auxiliary information into the analysis to help increase the plausibility of assumptions, narrow the range of the sensitivity analysis, or shrink the bounds.</p>
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<span class="site-title">DANIEL SCHARFSTEIN, ScD</span>
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<p>Professor of Biostatistics
</br>Bloomberg School of Public Health
</br>Johns Hopkins University
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Contact information:
<p> Johns Hopkins University
</br> Bloomberg School of Public Health
</br> 615 North Wolfe Street, E-3547
</br> Baltimore, MD 21205
</br>phone: 410-955-2420
</br> email: <a href="mailto:[email protected]">[email protected]</a></p>
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<p>Content updated on 12/12/2018.</p>
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