Identification of the Heterogeneous Survivor Average Causal Effect in Observational Studies

by   Yuhao Deng, et al.

Clinical studies are often encountered with truncation-by-death issues, which render the outcomes undefined. Statistical analysis based only on observed survivors may lead to biased results because the characters of survivors may differ greatly between treatment groups. Under the principal stratification framework, a meaningful causal parameter, the survivor average causal effect, in the always-survivor group can be defined. This causal parameter may not be identifiable in observational studies where the treatment assignment and the survival or outcome process are confounded by unmeasured features. In this paper, we propose a new method to deal with unmeasured confounding when the outcome is truncated by death. First, a new method is proposed to identify the heterogeneous conditional survival average causal effect based on a substitutional variable under monotonicity. Second, under additional assumptions, the survivor average causal effect on the whole population is identified. Furthermore, we consider estimation and inference for the conditional survivor average causal effect based on parametric and nonparametric methods. The proposed method can be used for post marketing drug safety or efficiency by utilizing real world data.


page 1

page 2

page 3

page 4


Using Survival Information in Truncation by Death Problems Without the Monotonicity Assumption

In some randomized clinical trials, patients may die before the measurem...

A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies

The Hazard Ratio (HR) is often reported as the main causal effect when s...

A general framework for causal classification

In many applications, there is a need to predict the effect of an interv...

VAINE: Visualization and AI for Natural Experiments

Natural experiments are observational studies where the assignment of tr...

Causal Inference under Data Restrictions

This dissertation focuses on modern causal inference under uncertainty a...

Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose

In this paper, we propose a system to estimate heterogeneous concurrent ...

Estimation of the Number Needed to Treat, the Number Needed to Expose, and the Exposure Impact Number with Instrumental Variables

The Number needed to treat (NNT) is an efficacy index defined as the ave...

Please sign up or login with your details

Forgot password? Click here to reset