- Continuing education on recent advances in biostatistics
|Talks focused on translating recent advances in biostatistics into practice.||Biostatistics Seminar Series|
During the 2014-15 academic year, the Harvard Catalyst Biostatistics Program will present a series of talks based on recent advances in biostatistics, but with a focus on translation of those ideas to biostatistical practice. Speakers will provide detailed examples of the application of methods, often including discussion of software, code, and worked examples.
If you missed our March 3, 2015, course with Dr. Tyler VanderWeele on Causal Mediation Analysis, you can watch the full event on our Past Seminars page.
Cory Zigler, PhD
May 19, 2015
3:30pm-5:30pm, Harvard T.H. Chan School of Public Health, FXB G12
Reservations are requested.
Comparative effectiveness research depends heavily on the analysis of a rapidly expanding universe of observational data made possible by the integration of health care delivery, the availability of electronic medical records, and the development of clinical registries. Despite extraordinary opportunities for research aimed at improving value in health care, a critical barrier to progress related to the lack of sound statistical methods that can address the multiple facets of estimating treatment effects in large, process-of-care databases with little a priori knowledge about confounding and treatment effect heterogeneity. When attempting to make causal inferences with such large observational data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set are necessary to properly adjust for confounding, or define subgroups experiencing heterogeneous treatment effects. To address these barriers, we discuss methods for estimating treatment effects that account for uncertainty in: 1) which of a high-dimensional set of observed covariates are confounders required to estimate causal effects; 2) which (if any) subgroups of the study population experience treatment effects that are heterogeneous with respect to observed factors. We outline two methods rooted in the tenets of Bayesian model averaging. The first prioritizes relevant variables to include in a propensity score model for confounding adjustment while acknowledging uncertainty in the propensity score specification. The second characterizes heterogeneous treatment effects by estimating subgroup-specific causal effects while accounting for uncertainty in the subgroup identification. Causal effects are averaged across multiple model specifications according to empirical support for confounding adjustment and existence of heterogeneous effects. We illustrate with a comparative effectiveness investigation of treatment strategies for brain tumor patients.
Rieke van der Graaf, PhD
May 20, 2015
4:00pm-5:00pm, Harvard T.H. Chan School of Public Health, FXB G11
May 27, 2015
12:30pm-1:30pm, Harvard T. H. Chan School of Public Health, Bldg 2, Rm 426
Please join us for this meeting of the Harvard Catalyst Biostatistics Journal Club. The leader of this meeting will be a member of our team at the Beth Israel Deaconness Medical Center. Please contact Letizia Allais for call-in information.