Improved Inference for Survival Heterogeneous Treatment Effect Using Trial and Observational Data

Music City Center 

While trials are attractive with guaranteed treatment randomization, they often lack the power for treatment effect evaluation due to limited and unrepresentative participants. Observational studies, with their accessible large-scale data, can help increase the study power and facilitate current trial analyses. However, combining data from both sources raises concerns about general exchangeability, as the absence of treatment randomization in observational studies leads to unmeasured confounding and obscures the true effect. When targeting the survival heterogeneous treatment effect, it is crucial to address this issue and formulate an integrative inference to improve efficiency. Under the Cox model, we introduce a confounding function to quantify bias between observed and causal effects, which can be identified by integrating the two data sources. Using a linear HTE structure for interpretability, we apply sieve approximation for nuisance functions and derive an integrative estimator via penalized loss minimization. This estimator achieves a promising convergence rate, asymptotic normality, and at least trial-level efficiency, validated through simulations and an application.

data integration

penalized loss function

sieve approximation

survival analysis 

Biopharmaceutical Section