gov3009-l October 2009

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Tchetgen on "Doubly robust estimation in a semi-parametric odds ratio model"

by Matt Blackwell

Hello all, We hope you can join us this Wednesday, October 28th when we will be excited to have Eric Tchetgen Tchetgen, Assistant Professor of Epidemiology at Harvard School of Public Health, presenting his work titled "Doubly robust estimation in a semi-parametric odds ratio model." Eric has provided the following abstract for the paper: We consider the doubly robust estimation of the parameters in a semi-parametric conditional odds ratio model characterizing the effect of an exposure in the presence of many confounders. We develop estimators that are consistent and asymptotically normal in a union model where either a prospective baseline density function or a retrospective baseline density function is correctly specified but not necessarily both. The case of a binary outcome is of particular interest, then our approach yields a doubly robust locally efficient estimator in a semi-parametric logistic regression model For general types of outcomes, we provide a strategy to obtain doubly robust estimators that are nearly locally efficient We illustrate the method in a simulation study and an application in statistical genetics. Finally, we briefly discuss extensions of the proposed method to the semi-parametric estimation of a parameter indexing an interaction between two exposures on the logistic scale, as well as extensions to the setting of a time-varying exposure in the presence of time-varying confounding. The workshop will begin, as usual, at 12 noon in room K354 of CGIS Knafel (1737 Cambridge St). A light lunch will be served and we usually wrap up around 1:30pm. We hope you can make it. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University email: mblackwell(a)iq.harvard.edu url: http://people.fas.harvard.edu/~blackwel/

14 years, 6 months

Eggers on "Electoral Rules, Opposition Scrutiny, and Policy Moderation in French Municipalities"

by Matt Blackwell

Hello all, Please join us this Wednesday October 21st when we will have a change in the schedule. We are happy to have Andy Eggers (Department of Government) presenting a talk titled "Electoral Rules, Opposition Scrutiny, and Policy Moderation in French Municipalities: An Application of the Regression Discontinuity Design." Andy has provided the following abstract for his talk: Regression discontinuity design (RDD) is a powerful and increasingly popular approach to causal inference that can be applied when treatment is assigned deterministically based on a continuous covariate. In this talk, I will present an application of RDD from French municipalities, where the system of electing the municipal council depends on whether the city's population is above or below 3500. First I show that cities above the population cutoff have fewer uncontested elections and more opposition representation on municipal councils, consistent with expectations. I then trace the effect of these political changes -- which amount to a heightening of the scrutiny imposed on the mayor -- on policy outcomes, providing evidence that more opposition scrutiny leads to more moderate policy. The workshop will start, as usual, at noon with a light lunch and wrap up by about 1:30. The workshop will convene in room K354 of CGIS (1737 Cambridge St). We hope you can make it. Cheers, matt.

14 years, 6 months

An on "Bayesian Propensity Score Estimation"

by Matt Blackwell

Hello all, We hope you can join us at the Applied Statistics workshop this Wednesday, October 14th at 12 noon, when we will be happy to have Weihua An, a graduate student in the Sociology Department here at Harvard. Weihua will be presenting "Bayesian Propensity Score Estimators: Simulations and Applications." He has provided the following abstract: Despite their popularity, conventional propensity score estimators (PSEs) do not take into account the estimation uncertainties in the propensity score into causal inference. This paper develops Bayesian propensity score estimators (BPSEs) to model the joint likelihood of both the outcome and the propensity score in one step, which naturally incorporate such uncertainties into causal inference. Simulations show that PSEs treating estimated propensity scores as if they were known will overestimate the variation in treatment e_ects and result in overly conservative inference, whereas BPSEs will provide corrected variance estimation and valid inference. Compared to other direct adjustment methods (E.g., Abadie and Imbens 2009), BPSEs are guaranteed to provide positive variance estimation, more reliable in small samples, and more flexible to contain complex propensity score models. To illustrate the proposed methods, BPSEs are applied to evaluating a job training program. The workshop will be in room K354 of CGIS, 1737 Cambridge St. The workshop starts at noon and usually wraps up around 1:30. There will be a light lunch. We hope you can make it. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University email: mblackwell(a)iq.harvard.edu url: http://people.fas.harvard.edu/~blackwel/

14 years, 6 months

Robins on "Optimal Treatment Regimes"

by Matt Blackwell

Hello all, Please join us this Wednesday, October 7th at the Applied Statistics workshop when we will be happy to have Jamie Robins, the Mitchell L. and Robin LaFoley Dong Professor of Epidemiology here at Harvard, who will be presenting on "Estimation of Optimal Treatment Strategies from Observational Data with Dynamic Marginal Structural Models." Jamie has passed along a related paper with the following abstract: We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction. A copy of the paper can be found here: http://www.people.fas.harvard.edu/~blackwel/extrapolation.pdf The workshop will being at noon with a light lunch and we usually wrap up by 1:30. We hope you can make it. Cheers, matt.

14 years, 6 months

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gov3009-l October 2009