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Title: Calibrated estimation of inverse probability of treatment weights for marginal structural models Authors:  Sean Yiu - University of Cambridge (United Kingdom)
Li Su - University of Cambridge (United Kingdom) [presenting]
Abstract: Marginal structural models (MSMs) with inverse probability-weighted estimators (IPWEs) are widely used to estimate causal effects of treatment sequences on longitudinal outcomes in the presence of time-varying confounding. However, IPWEs for MSMs can be inefficient and unstable if weights are estimated by maximum likelihood. To improve the performance of IPWEs, covariate balancing weight (CBW) methods have been proposed and recently extended to MSMs. However, existing CBW methods for MSMs are inflexible for practical use because they often do not handle non-binary treatments and longitudinal outcomes (instead of eventual outcomes at a study end). We propose a calibration approach to CBW estimation for MSMs that can accommodate (1) both binary and non-binary treatments, (2) eventual and longitudinal outcomes. We develop novel calibration restrictions by eliminating covariate associations with treatment assignment after weighting the observed data sample (i.e., to optimize covariate balance in finite samples). Two different methods are proposed to implement the calibration. We apply our method to a natural history study of HIV for estimating the effects of highly active antiretroviral therapy on CD4 cell counts over time. Extention to handle dependent censoring is also available.