Head & neck · 2025
Treatment Effect Estimation With Potential Outcomes for a Single-Arm Trial Compared With Historical Controls: A Case Study of Locally Recurrent Head and Neck Squamous Cell Carcinoma Patients Treated With Adjuvant Nivolumab.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Harun Nusrat, Sparapani Rodney, Laud Purushottam W, Sivaganesan Siva, Leddon Jennifer L, Haque Sulsal, Tang Alice L, Wise-Draper Trisha M
Аннотация
BACKGROUND: Time/resource constraints might preclude a randomized controlled trial. Single-arm oncology trials with historical controls are an alternative. With causal inference, treatment effect estimates can be computed in the absence of randomization.
METHODS: From a single-arm trial of 39 head and neck squamous cell carcinoma patients treated with adjuvant nivolumab, we compare 2-year disease-free survival (DFS) to untreated historical controls. We resort to the potential outcomes framework known as Rubin's causal model (RCM). For time-to-event outcomes, RCM relies upon survival analysis regression with baseline covariates. We contrast the average treatment effect (ATE) estimated by three survival methods: Cox proportional hazards (CPH) versus machine learning alternatives, random survival forests (RSF), and Bayesian Additive Regression Trees (BART).
RESULTS: The ATE in favor of nivolumab: CPH 0.202 (0.098-0.306); RSF 0.159 (0.070-0.248); and BART 0.268 (0.126-0.406).
CONCLUSIONS: The uncertainty is considerable, yet all three methods show nivolumab is superior to control.
METHODS: From a single-arm trial of 39 head and neck squamous cell carcinoma patients treated with adjuvant nivolumab, we compare 2-year disease-free survival (DFS) to untreated historical controls. We resort to the potential outcomes framework known as Rubin's causal model (RCM). For time-to-event outcomes, RCM relies upon survival analysis regression with baseline covariates. We contrast the average treatment effect (ATE) estimated by three survival methods: Cox proportional hazards (CPH) versus machine learning alternatives, random survival forests (RSF), and Bayesian Additive Regression Trees (BART).
RESULTS: The ATE in favor of nivolumab: CPH 0.202 (0.098-0.306); RSF 0.159 (0.070-0.248); and BART 0.268 (0.126-0.406).
CONCLUSIONS: The uncertainty is considerable, yet all three methods show nivolumab is superior to control.