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Department seminar by Shu Yang, Harvard T.H. Chan School of Public HealthExport this event to calendar

Thursday, January 7, 2016 — 4:00 PM EST

Optimal Estimation and Goodness-of-Fit Test of Coarse Structural Nested Mean Models, with Application to the Effect of one-year of HAART in HIV-Positive Patients

Coarse Structural Nested Mean Models (SNMMs) provide a useful tool to estimate treatment effects from longitudinal observational data with time-dependent confounders. Coarse SNMMs lead to a large class of estimators, which allows an optimal estimator to be derived under the conditions of well-specified models for the treatment effect, for treatment initiation, and for nuisance regression outcomes. The key assumption lies in a well-specified model for the treatment effect; however, there is no existing guidance on how to specify the treatment effect model, and model misspecification leads to biased estimators, preventing valid inference. To test whether the treatment effect model matches the data well, we derive a goodness-of-fit (GOF) test procedure based on overidentification restrictions tests (Sargan, 1958; Hansen, 1982). We show that the optimal estimator and the GOF statistic are doubly-robust in the sense that with a correct treatment effect model, if either the treatment initiation model or the nuisance regression outcome model is correctly specified, the estimator remains consistent and the GOF statistic has correct level. We demonstrate the empirical relevance of our methods using simulation designs based on an actual dataset. In addition, we apply the GOF test procedure to study how the initiation time of highly active antiretroviral treatment (HAART) after infection predicts the one-year treatment effect in HIV-positive patients with acute and early infection.

Location 
M3 - Mathematics 3
3127

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