Target trial emulation without matching: a more efficient approach for evaluating treatment effects

Thomas M. Menino Convention & Exhibition Center 

Defining a start of follow-up for untreated individuals is often challenging in target trial emulations that compare treatment to no treatment. A common solution is matching treated and untreated individuals, but this approach can implicitly alter the target population and reduce statistical efficiency. We propose a causal estimand for treatment effects based on cumulative incidences that marginalize over treatment initiation time and baseline covariates. We develop a simple g-computation estimator using Cox models, along with a one-step estimator that accommodates flexible machine learning models for nuisance estimation. We apply our proposed estimators in simulations and in a study to assess the effectiveness of the Pfizer-BioNTech COVID-19 vaccine to prevent SARS-CoV-2 infections in children 5-11 years old. In both settings, we find that our proposed estimators yield similar scientific inferences while providing significant efficiency gains over commonly used matching estimators. These results suggest that our framework may provide a practical and more efficient alternative to matching for target trial emulation studies.

Causal inference

Target trial emulation

Estimands

Vaccine effectiveness

Matching

Machine learning 

Section on Statistics in Epidemiology