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CeFH Biostatistical seminar: Estimating the treatment effect on the treated under time-dependent confounding

Presentation by Jon Michael Gran, Assistant Professor at the University of Oslo

About the CeFH Biostatistical seminar

The biostatistical seminar takes place on a monthly basis. The focus at these seminars is on methods and their mathematical backgrounds. Applications may also be presented. We invite a broad range of researcher from Norway and abroad to discuss various topics such as causal inference, Bayesian methods, variable selection, and more!

Statisticians ans data analysts from the Norwegian Institute of Public Health are invited at every meeting. People from the outside of the institute are also welcome to join. If you want to present your latest research, please contact William Denault.

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10. Apr 2019 - 13:00-14:00 | Seminar
Marcus Thranes gate 2, meeting room 2nd floor

About the speaker

Jon Michael Gran is associate professor at University of Oslo at the department of Biostatistics. His research focus is on causal inference and survival analysis.

About the presentation

When comparing time varying exposures in a non-randomized setting, one must often correct for time-dependent confounders that influence treatment choice over time and that are themselves influenced by treatment. In a JRSS-C paper from 2018 we suggested a new two-step procedure, based on additive hazard regression and linear increments models, for handling such confounding when estimating average treatment effects on the treated. The method was applied to data from the Swiss HIV Cohort Study, estimating the effect of antiretroviral treatment on time to acquired immune deficiency syndrome or death, and in a simulation study. The method is easy to implement by using available software packages in R and can also be used for mediation analysis.

In this talk I will give a brief introduction to this particular method and, more generally, discuss the definition and estimation of various causal target estimands in situations with time-dependent confounding (that is; the treatment effect on the treated and other alternatives).