Regression modeling of cumulative incidence function for left-truncated right-censored competing risks data: A modified pseudo-observation approach

Statistical methods have been developed for regression modeling of the cumulative incidence function (CIF) given left-truncated right-censored competing risks data. Nevertheless, existing methods typically involve complicated weighted estimating equations or non parametric conditional likelihood function and often require a restrictive assumption that censoring and/or truncation times are independent of failure time. The pseudo-observation (PO) approach has been used in regression modeling of CIF for right-censored competing risks data under covariate-independent censoring or covariate-dependent censoring. We extend this approach to left-truncated right-censored competing risks data and propose to directly model the CIF based on POs, under general truncation and censoring mechanisms. We adjust for covariate-dependent truncation and/or covariate-dependent censoring by incorporating covariate-adjusted weights into the inverse probability weighted (IPW) estimator of the CIF. We derive large sample properties of the proposed estimators under reasonable model assumptions and regularity conditions and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed method to a cohort study on pregnancy exposed to coumarin derivatives.