Faculty Bibliography

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.

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.

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.

Increasing evidence implicates sex and endocrine aging effects on brain bioenergetic aging in the greater lifetime risk of Alzheimer's disease (AD) in women. We conducted ³¹Phosphorus Magnetic Resonance Spectroscopy (³¹P-MRS) to assess the impact of sex and menopause on brain high-energy phosphates [adenosine triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi)] and membrane phospholipids [phosphomonoesters/phosphodiesters (PME/PDE)] in 216 midlife cognitively normal individuals at risk for AD, 80% female. Ninety-seven participants completed amyloid-beta (Aβ) ¹¹C-PiB PET. Women exhibited higher ATP utilization than men in AD-vulnerable frontal, posterior cingulate, fusiform, medial and lateral temporal regions (p < 0.001). This profile was evident in frontal cortex at the pre-menopausal and peri-menopausal stage and extended to the other regions at the post-menopausal stage (p = 0.001). Results were significant after multi-variable adjustment for age, APOE-4 status, midlife health indicators, history of hysterectomy/oophorectomy, use of menopause hormonal therapy, and total intracranial volume. While associations between ATP/PCr and Aβ load were not significant, individuals with the highest Aβ load were post-menopausal and peri-menopausal women with ATP/PCr ratios in the higher end of the distribution. No differences in Pi/PCr, Pi/ATP or PME/PDE were detected. Outcomes are consistent with dynamic bioenergetic brain adaptations that are associated with female sex and endocrine aging.

There is a growing literature on finding rules by which to assign treatment based on an individual's characteristics such that a desired outcome under the intervention is maximized. A related goal entails identifying a subpopulation of individuals predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators), perhaps even in the presence of a predicted beneficial total treatment effect. In some cases, the implications of a likely harmful indirect effect may outweigh an anticipated beneficial total treatment effect, and would motivate further discussion of whether to treat identified individuals. We build on the mediation and optimal treatment rule literatures to propose a method of identifying a subgroup for which the treatment effect through the mediator is expected to be harmful. Our approach is nonparametric, incorporates post-treatment confounders of the mediator-outcome relationship, and does not make restrictions on the distribution of baseline covariates, mediating variables, or outcomes. We apply the proposed approach to identify a subgroup of boys in the MTO housing voucher experiment who are predicted to have a harmful indirect effect of housing voucher receipt on subsequent psychiatric disorder incidence through aspects of their school and neighborhood environments.