Explaining differential effects of medication for opioid use disorder using a novel approach incorporating mediating variables

Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention

OBJECTIVE

A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.

METHODS

This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.

RESULTS

The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score ( P < 0.001), used cocaine on more days over the prior 30 days than other quartiles ( P < 0.001), and had highest proportions with alcohol and cocaine use disorder ( P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference ( P = 0.02) and all experiencing homelessness ( P < 0.001).

CONCLUSIONS

Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and static interventions and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by exposure. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the exposure and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether an exposure is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by exposure. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open-source software, the $\texttt{medshift}$$\texttt{R}$ package, implementing the proposed methodology. Application of our (in)direct effects and their nonparametric estimators is illustrated using data from a comparative effectiveness trial examining the direct and indirect effects of pharmacological therapeutics on relapse to opioid use disorder.

A scoping review of psychiatric conditions associated with chronic pain in the homeless and marginally housed population

The present review sought to examine and summarise the unique experience of concurrent pain and psychiatric conditions, that is often neglected, within the population of homeless individuals. Furthermore, the review examined factors that work to aggravate pain and those that have been shown to improve pain management. Electronic databases (MEDLINE, EMBASE, psycINFO, and Web of Science) and the grey literature (Google Scholar) were searched. Two reviewers independently screened and assessed all literature. The PHO MetaQAT was used to appraise quality of all studies included. Fifty-seven studies were included in this scoping review, with most of the research being based in the United States of America. Several interacting factors were found to exacerbate reported pain, as well as severely affect other crucial aspects of life that correlate directly with health, within the homeless population. Notable factors included drug use as a coping mechanism for pain, as well as opioid use preceding pain; financial issues; transportation problems; stigma; and various psychiatric disorders, such as post-traumatic stress disorder, depression, and anxiety. Important pain management strategies included cannabis use, Accelerated Resolution Therapy for treating trauma, and acupuncture. The homeless population experiences multiple barriers which work to further impact their experience with pain and psychiatric conditions. Psychiatric conditions impact pain experience and can work to intensify already adverse health circumstances of homeless individuals.