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.

Tachol1 QTL on mouse chromosome 1 is responsible for hypercholesterolemia and diet-induced obesity.

Hypercholesterolemia raises the risk for cardiovascular complications and overall health. Hypercholesterolemia is common, affecting 10% of the general population of the US, and heritable. Most individuals with hypercholesterolemia have a polygenic predisposition to the condition. Previously we identified a quantitative trait locus, Tachol1, linked to hypercholesterolemia on mouse chromosome 1 (Chr1) in a cross between C57BL/6J (B6) and TALLYHO/JngJ (TH) mice, a polygenic model for human obesity, type 2 diabetes and hyperlipidemia. Subsequently, using congenic mice that carry a TH-derived genomic segment of Chr1 on a B6 background, we demonstrated that the distal segment of Chr1, where Tachol1 maps, is necessary to cause hypercholesterolemia, as well as diet-induced obesity. In this study, we generated overlapping subcongenic lines to the distal segment of congenic region and characterized subcongenic mice carrying the smallest TH region of Tachol1, ~ 16.2 Mb in size (B6.TH-Chr1-16.2 Mb). Both male and female B6.TH-Chr1-16.2 Mb mice showed a significantly increased plasma total cholesterol levels compared to B6 on both chow and high fat (HF) diet. B6.TH-Chr1-16.2 Mb mice also had greater fat mass than B6 on HF diet, without increasing food intake. The gene and protein expression levels of absent in melanoma 2 (Aim2) gene were significantly upregulated in B6.TH-Chr1-16.2 Mb mice compared to B6. In summary, we confirmed the effect of Tachol1 on hypercholesterolemia and diet-induced obesity using subcongenic analysis.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

Independent and joint contributions of physical disability and chronic pain to incident opioid use disorder and opioid overdose among Medicaid patients.

BACKGROUND: Chronic pain has been extensively explored as a risk factor for opioid misuse, resulting in increased focus on opioid prescribing practices for individuals with such conditions. Physical disability sometimes co-occurs with chronic pain but may also represent an independent risk factor for opioid misuse. However, previous research has not disentangled whether disability contributes to risk independent of chronic pain. METHODS: Here, we estimate the independent and joint adjusted associations between having a physical disability and co-occurring chronic pain condition at time of Medicaid enrollment on subsequent 18-month risk of incident opioid use disorder (OUD) and non-fatal, unintentional opioid overdose among non-elderly, adult Medicaid beneficiaries (2016-2019). RESULTS: We find robust evidence that having a physical disability approximately doubles the risk of incident OUD or opioid overdose, and physical disability co-occurring with chronic pain increases the risks approximately sixfold as compared to having neither chronic pain nor disability. In absolute numbers, those with neither a physical disability nor chronic pain condition have a 1.8% adjusted risk of incident OUD over 18 months of follow-up, those with physical disability alone have an 2.9% incident risk, those with chronic pain alone have a 3.6% incident risk, and those with co-occurring physical disability and chronic pain have a 11.1% incident risk. CONCLUSIONS: These findings suggest that those with a physical disability should receive increased attention from the medical and healthcare communities to reduce their risk of opioid misuse and attendant negative outcomes.

Has the opening of Amazon fulfillment centers affected demand for disability insurance?

An estimated 17.6% of blue-collar, manufacturing jobs were lost in the United States between 1970 and 2016. These jobs, often union-represented, provided relatively generous pay and benefits, creating a path to the middle class for individuals without a four-year college degree. Evidence suggests the closure of manufacturing facilities and resulting decline in economic opportunity increased demand for disability insurance (SSDI) among blue-collar workers. In recent years, the opening of Amazon Fulfillment Centers (FCs) has accelerated around the country, driving a wave of blue-collar job creation. We estimated the extent to which the opening of FCs affected SSDI application rates, including rates of approvals and denials, using a synthetic control group approach. We found that FC openings were associated with a 1.4% reduction in the SSDI application rate over the subsequent three years, translating to 5,528 fewer applications per year across commuting zones with an FC opening. Our findings are consistent with FC openings improving economic opportunities in local labor markets, though our confidence intervals were wide and included the null.

Optimally Choosing Medication Type for Patients With Opioid Use Disorder.

Patients with opioid use disorder (OUD) tend to get assigned to one of 3 medications based on the treatment program to which the patient presents (e.g., opioid treatment programs tend to treat patients with methadone, while office-based practices tend to prescribe buprenorphine). It is possible that optimally matching patients with treatment type would reduce the risk of return to regular opioid use (RROU). We analyzed data from 3 comparative effectiveness trials from the US National Institute on Drug Abuse Clinical Trials Network (CTN0027, 2006-2010; CTN0030, 2006-2009; and CTN0051 2014-2017), in which patients with OUD (n = 1,459) were assigned to treatment with either injection extended-release naltrexone (XR-NTX), sublingual buprenorphine-naloxone (BUP-NX), or oral methadone. We learned an individualized rule by which to assign medication type such that risk of RROU during 12 weeks of treatment would be minimized, and then estimated the amount by which RROU risk could be reduced if the rule were applied. Applying our estimated treatment rule would reduce risk of RROU compared with treating everyone with methadone (relative risk (RR) = 0.79, 95% confidence interval (CI): 0.60, 0.97) or treating everyone with XR-NTX (RR = 0.71, 95% CI: 0.47, 0.96). Applying the estimated treatment rule would have resulted in a similar risk of RROU to that of with treating everyone with BUP-NX (RR = 0.92, 95% CI: 0.73, 1.11).

Buprenorphine & methadone dosing strategies to reduce risk of relapse in the treatment of opioid use disorder.

BACKGROUND: Although there is consensus that having a "high-enough" dose of buprenorphine (BUP-NX) or methadone is important for reducing relapse to opioid use, there is debate about what this dose is and how it should be attained. We estimated the extent to which different dosing strategies would affect risk of relapse over 12 weeks of treatment, separately for BUP-NX and methadone. METHODS: This was a secondary analysis of three comparative effectiveness trials. We examined four dosing strategies: 1) increasing dose in response to participant-specific opioid use, 2) increasing dose weekly until some minimum dose (16 mg BUP, 100 mg methadone) was reached, 3) increasing dose weekly until some minimum and increasing dose in response to opioid use thereafter (referred to as the "hybrid strategy"), and 4) keeping dose constant after the first 2 weeks of treatment. We used a longitudinal sequentially doubly robust estimator to estimate contrasts between dosing strategies on risk of relapse. RESULTS: For BUP-NX, increasing dose following the hybrid strategy resulted in the lowest risk of relapse. For methadone, holding dose constant resulted in greatest risk of relapse; the other three strategies performed similarly. For example, the hybrid strategy reduced week 12 relapse risk by 13 % (RR: 0.87, 95 %CI: 0.83-0.95) and by 20 % (RR: 0.80, 95 %CI: 0.71-0.90) for BUP-NX and methadone respectively, as compared to holding dose constant. CONCLUSIONS: Doses should be targeted toward minimum thresholds and, in the case of BUP-NX, raised when patients continue to use opioids.

Hypertension resolution after adrenalectomy for primary hyperaldosteronism: Which is the best predictive model?

BACKGROUND: We aimed to compare the predictive performance of three distinct clinical models purported to predict the resolution of aldosteronoma-associated hypertension after adrenalectomy. METHODS: A tri-institutional database of aldosteronoma patients who underwent adrenalectomy between 2004 and 2019 was retrospectively reviewed. The three models of interest incorporate various preoperative clinical factors, such as age and sex. The predictive accuracy, as measured by area under the curve of receiver operator characteristic, was estimated. Receiver operator characteristic was evaluated across the whole cohort, then stratified by treatment location. RESULTS: A total of 200 patients were included (91 American, 109 French). The clinicodemographic variables between groups were similar; the French cohort had a lower mean body mass index (P = .02). The overall complete clinical resolution of hypertension after adrenalectomy for the entire data set was 45.5% (n = 91). The regression coefficients in the Utsumi et al (2014) Japanese model produced a superior overall area under the curve (0.78, 95% confidence interval [CI] [0.71-0.84]). This model also performed best when the cohort was stratified by treatment location (French area under the curve = 0.74, 95% CI [0.64-0.83], US area under the curve = 0.82, 95% CI [0.72-0.91]). CONCLUSION: When comparing three predictive models of aldosteronoma-associated hypertension resolution after adrenalectomy, the Utsumi et al model demonstrated the highest predictive validity across all cohorts. Counseling based on this model regarding probability of cure is recommended.