Data Fusion Estimators

Uses

How it works

Evidence quality

Highlighted claims

• Participant-level data fusion can estimate counterfactual clinical incidence curves for vaccine regimens evaluated only through immunologic outcomes, subject to causal assumptions. — Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
• The estimators are semiparametrically efficient when nuisance functions are correctly specified. — Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
• The proposed one-step estimators can use nuisance estimates from parametric, semiparametric, or machine-learning models. — Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
• With flexible learners, the paper proposes cross-fitted debiased machine learning using K-fold sample splitting. — Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
• Non-monotone cumulative incidence estimates can be projected onto non-decreasing functions without changing asymptotic behavior. — Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data