Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation

TL;DR

This paper introduces a novel regression-based approach, WPOM, for estimating dynamic treatment regimes with ordinal outcomes considering household interference, validated through simulations and applied to smoking cessation data.

Contribution

The paper develops WPOM, a doubly-robust method for single-stage DTR estimation with ordinal outcomes, and extends it to multi-stage settings accounting for household interference.

Findings

WPOM is approximately doubly robust in simulations.

Extended WPOM (dWPOM) effectively models household interference.

Application to tobacco study provides optimal household treatment strategies.

Abstract

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes (DTRs), which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, DTR estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model (WPOM): a regression-based, approximate doubly-robust approach to single-stage DTR estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of WPOM with our adjusted weights via simulation studies. We further extend WPOM to multi-stage DTR estimation with household interference, namely dWPOM (dynamic WPOM). Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.