Dynamic treatment regimes with interference

Abstract

Precision medicine describes health care where patient‐level data are used to inform treatment decisions. Within this framework, dynamic treatment regimes (DTRs) are sequences of decision rules that take individual patient information as input data and then output treatment recommendations. DTR estimation from observational data typically relies on the assumption of no interference: i.e., the outcome of one individual is unaffected by the treatment assignment of others. However, in many social network contexts, such as friendship or family networks, and for many health concerns, such as infectious diseases, this assumption is questionable. We investigate the DTR estimation method of dynamic weighted ordinary least squares (dWOLS), which boasts of easy implementation and the so‐called double‐robustness property, but relies on the assumption of no interference. We define a network propensity function and build on it to establish an implementation of dWOLS that remains doubly robust under interference associated with network links. The method's properties are demonstrated via simulation and applied to data from the Population Assessment of Tobacco and Health (PATH) study to investigate cigarette dependence within two‐person household networks.

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

“…(3) Joint propensity score model 1 : 𝜋 a s a r (x s , x r ) = ℙ(A s = a s , A r = a r | x s , x r ). Further, let w std denote 'standard' interference-aware balancing weights, 1 which satisfy:…”

“…In this context, we employ the term 'interference-aware balancing weights' to differentiate them from those in noninterference settings, where the weights that are not 'interference-aware' only involve the common propensity score (see the balancing weights in Wallace and Moodie 5 ). The work by Jiang et al, 1 where the focus is primarily on balancing weights considering network interference, outlined the criteria for balancing weights in network settings. Equation (3) mentioned above represents a special case of their balancing weights criteria specifically adapted for household settings.…”

“…To construct the balancing weights for the proposed POM, as introduced in Section 2.4, we estimate the marginal propensity scores, the pairwise odds ratios (𝜏 sr ), and the joint propensity scores. We first choose covariates at the individual level for the marginal treatment propensity models, based on the previous PATH studies of Benmarhnia et al 12 and Jiang et al, 1 as x 𝛼 j = (x j1 , x j2 , x 3 , x 4 , x 5j , x 6 ) ⊤ , namely, age, education, non-Hispanic, race, 'plan to quit', and sex. Then, we employ logistic regression to acquire marginal treatment propensity scores.…”

“…Dynamic treatment regimes (DTRs), as a statistical framework for precision medicine, provide individualized treatment recommendations based on patients' individual information. Recently, the consideration of interference, where one individual's outcome is possibly affected by others' treatment, has gained importance in estimating optimal DTRs, [1][2][3] which are sequences of treatment rules that yield the best-expected health outcome across a population.…”

“…Lately, some researchers, such as Su et al., 2 Jiang et al. 1 and Park et al., 4 have focused on optimal DTR estimation in the presence of interference. In such cases, treatment-decision rules should involve others’ information such as treatments and covariates.…”