Applied Stats Workshop (Gov 3009)


Wednesday, March 22, 2017, 12:00pm to 1:30pm


CGIS Knafel K354

The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.  Free lunch is provided.

Elizabeth Stuart (Johns Hopkins) presents 



Title: Estimating population effects: Assessing and enhancing the  generalizability of randomized trials to target populations



With increasing attention being paid to the relevance of studies for real-world practice (such as in education, international development, and comparative effectiveness research), there is also growing interest in external validity and assessing whether the results seen in randomized trials would hold in target populations. While randomized trials yield unbiased estimates of the effects of interventions in the sample of individuals (or physician practices or schools) in the trial, they do not necessarily inform about what the effects would be in some other, potentially somewhat different, population.  While there has been increasing discussion of this limitation of traditional trials, relatively little statistical work has been done developing methods to assess or enhance the external validity of randomized trial results.  This talk will first provide empirical data on the potential size of external validity bias in education research.  It will then discuss design and analysis methods for assessing and increasing external validity, as well as general issues that need to be considered when thinking about external validity.  The primary analysis approach discussed will be a reweighting approach that equates the sample and target population on a set of observed characteristics.  Underlying assumptions, performance in simulations, and limitations will be discussed.  Implications for how future studies should be designed (and what data needs to be collected) in order to enhance the ability to assess generalizability will also be discussed.