Continuous-time structural failure time model for intermittent treatment

TL;DR

This paper introduces a novel continuous-time structural failure time model that utilizes all treatment dispensation data to accurately estimate treatment effects on failure times, addressing biases from previous methods.

Contribution

The work develops doubly robust, locally efficient estimators for treatment effects using continuous-time models that incorporate intermittent treatment data, overcoming current limitations.

Findings

Estimators are doubly robust and locally efficient.

Method effectively handles dependent censoring.

Utilizes all treatment dispensation information for accurate estimation.

Abstract

The intermittent intake of treatment is commonly seen in patients with chronic disease. For example, patients with atrial fibrillation may need to discontinue the oral anticoagulants when they experience a certain surgery and re-initiate the treatment after the surgery. As another example, patients may skip a few days before they refill a treatment as planned. This treatment dispensation information (i.e., the time at which a patient initiates and refills a treatment) is recorded in the electronic healthcare records or claims database, and each patient has a different treatment dispensation. Current methods to estimate the effects of such treatments censor the patients who re-initiate the treatment, which results in information loss or biased estimation. In this work, we present methods to estimate the effect of treatments on failure time outcomes by taking all the treatment dispensation information. The developed methods are based on the continuous-time structural failure time model, where the dependent censoring is tackled by inverse probability of censoring weighting. The estimators are doubly robust and locally efficient.