Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

Pain Management Treatments and Opioid Use Disorder Risk in Medicaid Patients.

INTRODUCTION: People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels. METHODS: Data were analyzed in 2024 from 2 chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: those with (1) chronic pain and physical disability (CPPD) (N=6,133) or (2) chronic pain without disability (CP) (N=67,438). Nine pain management treatments were considered: prescription opioid (1) dose and (2) duration; (3) number of opioid prescribers; opioid co-prescription with (4) benzo- diazepines, (5) muscle relaxants, and (6) gabapentinoids; (7) nonopioid pain prescription, (8) physical therapy, and (9) other pain treatment modality. The outcome was OUD risk. RESULTS: Having opioids co-prescribed with gabapentin or benzodiazepine was statistically significantly associated with a 37-45% increased OUD risk for the CP subgroup. Opioid dose and duration also were significantly associated with increased OUD risk in this subgroup. Physical therapy was significantly associated with an 18% decreased risk of OUD in the CP subgroup. DISCUSSION: Coprescription of opioids with either gabapentin or benzodiazepines may substantially increase OUD risk. More positively, physical therapy may be a relatively accessible and safe pain management strategy.

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.

Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.