The Section on Statistics in Epidemiology invites applications for young investigator awards from junior researchers who will present papers at the 2012 Joint Statistical Meetings (JSM) in San Diego, California. The awards honor the best papers in statistics in epidemiology presented at JSM 2012 and are open to all current graduate students in statistics, biostatistics, and epidemiology and recent graduates who earned their degree within the past two years. Each award consists of $500 and a certificate. Additionally, a reception will be held at the San Diego meeting to honor award recipients.
Preference will be given to papers with both methodologic contributions and substantive epidemiological applications. Jointly authored papers are acceptable, but the applicant is expected to be the lead author and present the paper at JSM, though the presentation does not have to be in a session sponsored by the Section on Statistics in Epidemiology, nor must applicants be current members of the section.
To apply, you must have already submitted an abstract for JSM 2012. Papers to be presented should be submitted by June 1, along with a cover letter stating where you are a current student or your year of graduation if you are a recent graduate, to Jaya Satagopan, section secretary/treasurer, at satagopj@mskcc.org. Questions about the award also can be addressed to Melissa D. Begg, section chair, at mdb3@columbia.edu.
JSM Short Course
The Section on Statistics in Epidemiology will sponsor the following short course during JSM 2012:
Propensity Score Matching in R
Instructor: Ben Hansen, University of Michigan
In an observational study, the researcher attempts to understand effects of an intervention upon people, without controlling which receive the intervention. Absent random assignment, findings of a difference between groups receiving and not receiving the intervention will inevitably be equivocal, explicable as consequences either of the intervention or pre-existing differences between the groups.
Propensity score matching aims to strip observed covariates of their ability to confound a comparison, in this way disambiguating findings the study might produce. It is particularly beneficial for nonrandomized comparisons in which there are many measured, potentially important baseline differences, but nonetheless relatively simple and widely accessible analytic procedures are desired. Rich diagnostics are available to guide the selection of the match and enrich the final presentation of results. Although it leaves open the possibility of confounding in terms of unobserved variables, its handling of observed covariates is sufficiently compelling to make it a popular tool in observational research—the 1983 paper introducing it now has, according to Google, nearly 7,000 citations.
This course introduces propensity score matching and related ideas conceptually and methodologically, with complementary programming exercises using R and its “optmatch” and “RItools” add-on packages.
Check the online program for time and location information.
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