Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer

Randomized Controlled Trials (RCT) are the gold standard for estimating treatment effects but some important situations in cancer care require treatment effect estimates from observational data. We developed Proxy based individual treatment effect modeling in cancer (PROTECT) to estimate treatment effects from observational data when there are unobserved confounders, but proxy measurements of these confounders exist. We identified an unobserved confounder in observational cancer research: overall fitness. Proxy measurements of overall fitness exist like performance score, but the fitness as observed by the treating physician is unavailable for research. PROTECT reconstructs the distribution of the unobserved confounder based on these proxy measurements to estimate the treatment effect. PROTECT was applied to an observational cohort of 504 stage III non-small cell lung cancer (NSCLC) patients, treated with concurrent chemoradiation or sequential chemoradiation. Whereas conventional confounding adjustment methods seemed to overestimate the treatment effect, PROTECT provided credible treatment effect estimates.

Automated summary

Randomized Controlled Trials (RCT) are the gold standard for estimating treatment effects, but in certain situations, treatment effect estimates from observational data are needed. PROTECT is a method developed to estimate treatment effects from observational data when there are unobserved confounders, but proxy measurements of these confounders exist. In an observational cohort of 504 stage III non-small cell lung cancer (NSCLC) patients, PROTECT provided credible treatment effect estimates, unlike conventional confounding adjustment methods which seemed to overestimate the treatment effect.