||5R03CA208387-02 Interpret this number
||University Of Colorado Denver
||Understanding Causal Effects of a Treatment on Survival in Observational Studies with Unmeasured Confounding and an Application to Effect of Pmrt on Breast Cancer Patients
Many clinical studies on survival outcomes based on observational data are challenged by unmeasured
confounding. Instrumental variable (IV) methods are popular approaches for dealing with both measured and
unmeasured confounding and are increasingly being adopted in clinical studies. The IV methods are very well
developed for linear and some generalized linear regression models, however, the IV methods are not well
developed for survival outcomes, especially for the Cox proportional hazards model which is the most popular
regression model for censored survival data.
Recently, there is a widespread use of two stage residual inclusion (2SRI) method offered by Terza et al.
(2008) for nonlinear models, and 2SRI has been the method of choice for analyzing Cox model using IV in
clinical studies. However, the causal parameter using 2SRI is only identified under a homogeneity assumption
that goes beyond the assumptions of IV, and Wan et al. (2015) demonstrated that under standard IV
assumptions, 2SRI could fail to consistently estimate the causal hazard ratio.
In this proposal, we seek to develop a novel IV method to obtain consistent estimates of causal treatment
effects for survival outcomes under standard IV assumptions, while accounting for unmeasured confounding
and censoring. Specifically we will evaluate the effectiveness of postmastectomy radiotherapy (PMRT) on
survival of breast cancer patients with 1-3 positive lymph nodes and tumors ≤ 5 cm (AJCC pT1-2pN1), a group
of patients on whom the use of PMRT remains controversial. We expect that our project will have broad impact
and wide applications in comparative effectiveness studies on survival outcomes, and our method will help
clinical researchers improve their understanding about the potential risks and benefits of alternative treatments
by obtaining trustworthy inference for survival data from observational studies.
Using survival information in truncation by death problems without the monotonicity assumption.
, Ding P.
Biometrics, 2018 12; 74(4), p. 1232-1239.