https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-015-0544-8
Highly Accessed Articles
EDITORIAL
What makes an academic paper useful for health policy?
Christopher J M Whitty
Tag Archives: journal club
To read: What makes an academic paper useful for health policy?
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Reusable Holdout
Cool paper, cool idea, ICYMI:
From: Mabry, Patricia L
Sent: Thursday, January 14, 2016 5:51 AM
Subject: [iuni_systems_sci-l] Article of interest: reusable holdout method
Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015). The reusable holdout: Preserving validity in adaptive data analysis.Science, 349(6248), 636-638.
Misapplication of statistical data analysis is a common cause of spurious discoveries in
scientific research. Existing approaches to ensuring the validity of inferences drawn from data
assume a fixed procedure to be performed, selected before the data are examined. In common
practice, however, data analysis is an intrinsically adaptive process, with new analyses
generated on the basis of data exploration, as well as the results of previous analyses on the
same data. We demonstrate a new approach for addressing the challenges of adaptivity based
on insights from privacy-preserving data analysis. As an application, we show how to safely
reuse a holdout data set many times to validate the results of adaptively chosen analyses.
http://science.sciencemag.org/content/349/6248/636.full-text.pdf+html
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Journal Club: Counter-measures for Implicit Bias
This long-read from vox.com would be 13 pages if I printed it, but it is for a general audience, so it’s not like a 13 page research paper.
http://www.vox.com/identities/2016/11/15/13595508/racism-trump-research-study
Linked from the link is this amazing video:
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Filed under diversity and inclusion
Journal Club: Reducing Bias in Academic Search Committees
Short article from the kick-off of a new IHME journal club, with a focus on diversity and health disparities: [link]
Topics that bubbled up in discussion: composition of search committees, pipeline issues and other barriers to attracting diverse candidates, the scale of the problem with systemic racism.
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Journal Club: Ten Frequently Asked Questions About Implicit Measures and Their Frequently Supposed, But Not Entirely Correct Answers
Second edition of the Diversity Lunch Discussion journal club, with a focus on the Implicit Association Test. Many participants also *took* an IAT—Rose suggests you try taking the Race IAT before our discussion tomorrow: https://implicit.harvard.edu/implicit/takeatest.html
So much knowledge and expertise in this group.
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CVRS analysis in Kiribati
To read: Mortality and life expectancy in Kiribati based on analysis of reported deaths
http://pophealthmetrics.biomedcentral.com/articles/10.1186/s12963-016-0072-6
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Filed under global health
Article I’m interested in: “Machine Learning and the Profession of Medicine”
Darcy AM, Louie AK, Roberts L. Machine Learning and the Profession of Medicine. JAMA. 2016;315(6):551-552. doi:10.1001/jama.2015.18421.
> Must a physician be human? …
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To read in JAMA: Trends in Prescription Drug Use Among Adults in the United States From 1999-2012
Trends in Prescription Drug Use Among Adults in the United States From 1999-2012
http://jama.jamanetwork.com/article.aspx?articleID=2467552
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Journal Club: Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.
Last week, we read Chien et al, Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S. I’ve been very interested in these sort of “small-areas” spatial statistical methods recently, so it was good to see what is out there as the state of the art. I think I’ve got something to contribute along these lines some day soon.
Maps in small multiples look just lovely, too:
Filed under statistics
Journal Club: Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation
While I’m catching up on journal club reading, two weeks ago we discussed Chu et al, Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation, which takes on the question of how to decide when it is safe to stop a massive disease elimination program. This work must rely on some cool mathematical epi modeling, to say how many years of what level of coverage is necessary before you can hope the LF is gone.
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Filed under disease modeling, global health
Healthy Algorithms 