Faculty Bibliography

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.

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.

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).

BACKGROUND:Primary care "teamlets" in which a staff member and physician consistently work together might provide a simple, cost-effective way to improve care, with or without insertion within a team. OBJECTIVE:To determine the prevalence and performance of teamlets and teams. DESIGN:Cross-sectional observational study linking survey responses to Medicare claims. PARTICIPANTS:Six hundred eighty-eight general internists and family physicians. INTERVENTIONS:Based on survey responses, physicians were assigned to one of four teamlet/team categories (e.g., teamlet/no team) and, in secondary analyses, to one of eight teamlet/team categories that classified teamlets into high, medium, and low collaboration as perceived by the physician (e.g., teamlet perceived-high collaboration/no team). MAIN MEASURES:Descriptive: percentage of physicians in teamlet/team categories. OUTCOME MEASURES:physician burnout; ambulatory care sensitive emergency department and hospital admissions; Medicare spending. KEY RESULTS:77.4% of physicians practiced in teamlets; 36.7% in teams. Of the four categories, 49.1% practiced in the teamlet/no team category; 28.3% in the teamlet/team category; 8.4% in no teamlet/team; 14.1% in no teamlet/no team. 15.7%, 47.4%, and 14.4% of physicians practiced in perceived high-, medium-, and low-collaboration teamlets. Physicians who practiced neither in a teamlet nor in a team had significantly lower rates of burnout compared to the three teamlet/team categories. There were no consistent, significant differences in outcomes or Medicare spending by teamlet/team or teamlet perceived-collaboration/team categories compared to no teamlet/no team, for Medicare beneficiaries in general or for dual-eligible beneficiaries. CONCLUSIONS:Most general internists and family physicians practice in teamlets, and some practice in teams, but neither practicing in a teamlet, in a team, or in the two together was associated with lower physician burnout, better outcomes for patients, or lower Medicare spending. Further study is indicated to investigate whether certain types of teamlet, teams, or teamlets within teams can achieve higher performance.

BACKGROUND/UNASSIGNED:Patients who have undergone solid organ transplants (SOT) have an increased risk for sepsis compared with the general population. Paradoxically, studies suggest that SOT patients with sepsis may experience better outcomes compared with those without a SOT. However, these analyses used previous definitions of sepsis. It remains unknown whether the more recent definitions of sepsis and modern analytic approaches demonstrate a similar relationship. METHODS/UNASSIGNED:Using the Weill Cornell-Critical Care Database for Advanced Research, we analyzed granular physiologic, microbiologic, comorbidity, and therapeutic data in patients with and without SOT admitted to intensive care units (ICUs). We used a survival analysis with a targeted minimum loss-based estimation, adjusting for within-group (SOT and non-SOT) potential confounders to ascertain whether the effect of sepsis, defined by sepsis-3, on 28-day mortality was modified by SOT status. We performed additional analyses on restricted populations. RESULTS/UNASSIGNED:We analyzed 28 431 patients: 439 with SOT and sepsis, 281 with SOT without sepsis, 6793 with sepsis and without SOT, and 20 918 with neither. The most common SOT types were kidney (475) and liver (163). Despite a higher severity of illness in both sepsis groups, the adjusted sepsis-attributable effect on 28-day mortality for non-SOT patients was 4.1% (95% confidence interval [CI], 3.8-4.5) and -14.4% (95% CI, -16.8 to -12) for SOT patients. The adjusted SOT effect modification was -18.5% (95% CI, -21.2 to -15.9). The adjusted sepsis-attributable effect for immunocompromised controls was -3.5% (95% CI, -4.5 to -2.6). CONCLUSIONS/UNASSIGNED:Across a large database of patients admitted to ICUs, the sepsis-associated 28-day mortality effect was significantly lower in SOT patients compared with controls.

BACKGROUND:) diagnosis code, but this code's validity remains unclear. METHODS: RESULTS: CONCLUSIONS:NIHSS scores were often missing, especially in less severe strokes, limiting the reliability of these codes for risk adjustment.

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient's residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70-0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71-0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79-0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79-0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite sample estimation of causal effects. We address the question: when estimating the effect of a treatment on an outcome, how much does the choice of nonparametric vs parametric estimation matter? Instead of answering this question with simulations that reflect a few chosen data scenarios, we propose a novel approach to compare estimators across a large number of datagenerating mechanisms drawn from nonparametric models with semi-informative priors. We apply this proposed approach and compare the performance of two nonparametric estimators (Bayesian adaptive regression tree and a targeted minimum loss-based estimator) to two parametric estimators (a logistic regression- based plug-in estimator and a propensity score estimator) in terms of estimating the average treatment effect across thousands of data-generating mechanisms. We summarize performance in terms of bias, confidence interval coverage, and mean squared error. We find that the two nonparametric estimators can substantially reduce bias as compared to the two parametric estimators in large-sample settings characterized by interactions and nonlinearities while compromising very little in terms of performance even in simple, small-sample settings.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

OBJECTIVE/UNASSIGNED:The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions. METHODS/UNASSIGNED:We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes. RESULTS/UNASSIGNED:Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge. CONCLUSIONS/UNASSIGNED:Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.