Faculty Bibliography

Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across prespecified patient populations. However, these analyses frequently suffer from small sample sizes, which limit the power to detect heterogeneous effects. Power can be improved by leveraging predictors of the outcome-that is, through covariate adjustment-as well as by borrowing external data from similar RCTs or observational studies. The benefits of covariate adjustment may be limited when the trial sample is small. Borrowing external data can increase the effective sample size and improve power, but it introduces two key challenges: (i) integrating data across sources can lead to model misspecification, and (ii) practical violations of the positivity assumption-where the probability of receiving the target treatment is near zero for some covariate profiles in the external data-can lead to extreme inverse-probability weights and unstable inferences, ultimately negating potential power gains. To account for these shortcomings, we present an approach to improving power in preplanned subgroup analyses of small RCTs that leverages both baseline predictors and external data. We propose de-biased estimators that accommodate parametric, machine learning (ML), and nonparametric Bayesian methods. To address practical positivity violations (PPVs), we introduce three estimators: A covariate-balancing approach, an automated de-biased machine learning (DML) estimator, and a calibrated-DML estimator. We show improved power in various simulations and offer practical recommendations for the application of the proposed methods. Finally, we apply them to evaluate the effectiveness of citalopram for negative symptoms in first-episode schizophrenia (FES) patients across subgroups defined by duration of untreated psychosis (DUP), using data from two small RCTs.

Kaplan-Meier analysis of time to first all-cause readmission over 12 months among older adults with ischemic heart disease randomized to 3 months of mobile health cardiac rehabilitation (mHealth-CR) or usual care. There was no significant difference in time to first readmission between groups (log-rank P = 0.26).

BACKGROUND:Accurate measurement of pharmacy access is central to understanding spatial inequities in healthcare availability. The two-step floating catchment area (2SFCA) method is widely used to integrate proximity and availability, yet key challenges remain unresolved in dense urban settings, including catchment sensitivity, and the lack of uncertainty quantification despite reliance on survey-based population estimates. OBJECTIVE:To assess catchment sensitivity to specification using a mode-adjusted 2SFCA approach while propagating uncertainty from the American Community Survey (ACS). METHODS:We conducted a cross-sectional analysis of pharmacy access across 2222 census tracts in New York City using multiple distance- and time-based catchments. We applied a mode-adjusted 2SFCA method that incorporates walking and driving accessibility by decomposing populations demand using tract-level transportation proportions from the ACS. Uncertainty in access estimates was quantified via hierarchical Monte Carlo simulation propagating the margins of errors from the ACS. We used cross-tabulation to quantify agreement in classifying tracts as having fewer than one pharmacy per 10,000 residents across transportation modes. RESULTS:Relative pharmacy access patterns were stable across a wide range of catchment specifications, indicating limited sensitivity to reasonable changes in distance or travel-time thresholds in this dense urban setting. The mode-adjusted approach increased median access and introduced additional variability by accounting for transportation differences. Uncertainty in access estimates was generally negligible but may matter where driving and walking catchments do not overlap. CONCLUSIONS:These findings suggest that, in New York City, catchment size and ACS uncertainty have limited impact on access estimates, while transportation differences and border effects play a larger role.

[This corrects the article DOI: 10.7759/cureus.113024.].

Background A landmark study of 1,600 Italian-Americans in Roseto, PA, challenged the prevailing view that high saturated fat intake was a major cause of myocardial infarction (MI). Despite similar rates of cigarette smoking and obesity, and even higher levels of saturated fat consumption compared to neighboring towns, Rosetans experienced far lower MI death rates. More than 50 years later, it remains uncertain whether Roseto's residents had better heart health than the average American and, if so, what protective factors may have been responsible. Methodology We compared MI deaths in Roseto and neighboring towns to the contemporaneous Framingham Heart Study cohort matched for age and sex. Results We found no evidence that MI deaths were lower in Roseto, PA, than in Framingham, MA when controlling for age and sex. While the role of social support in health has been established in subsequent studies, methodological issues, confounding factors, and biases challenge the validity of the Roseto study. Conclusions The dramatically lower MI and MI mortality rates among males in Roseto reflect biases in sampling and comparison populations, which also impacted the contrasting Diet-Heart Hypothesis that saturated fats cause heart disease. Although social support enhances health outcomes, the Roseto study neither supported nor refuted this connection.

PURPOSE/OBJECTIVE:Mobile health cardiac rehabilitation may improve access to care among older adults with ischemic heart disease, but engagement remains poorly understood. We analyzed weekly engagement data from the RESILIENT (Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting) trial, a large, randomized trial of mobile health cardiac rehabilitation in older adults conducted in the United States. METHODS:Data from 298 intervention participants were analyzed. Weekly engagement was scored from 0 to 11 based on exercise entry (7 points), communication with exercise therapist (2 points), video viewing (1 point), and blood pressure measurement (1 point). Latent class analysis identified digital engagement phenotypes. Participant characteristics were compared, and multivariable logistic regression identified factors associated with phenotype membership. RESULTS:Median age was 71.0 years, 28% were women, 23% were non-White, and 62% were enrolled after elective percutaneous coronary intervention. Latent class analysis identified 3 phenotypes: persistently low (n = 81), intermediate declining (n = 93), and persistently high (n = 124). Participants with persistently low engagement were more likely to be non-White (48% vs 12% vs 15%, P < .001), Medicaid enrolled (22% vs 8% vs 7%, P = .001), have less than high school education (16% vs 4% vs 3%, P < .001), have frailty phenotype (28% vs 10% vs 7%, P < .001), and have a greater mean number of comorbidities (3.1 vs 3.0 vs 2.6; P = .012). After adjustment, non-White race and frailty remained independently associated with low engagement. Improvement in 6-minute walk test distance varied: 20.8 m (low), 29.7 m (intermediate), and 54.5 m (high) (P = .003). CONCLUSIONS:Three distinct digital engagement phenotypes emerged. Persistently low engagement was more common among non-White and frail participants, underscoring ongoing disparities despite efforts to overcome the digital divide.

OBJECTIVE:Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases. MATERIALS AND METHODS/METHODS:This was a retrospective study of patients with heart failure (HF) at an academic health system using an algorithm that predicts pharmacy fill nonadherence to evidence-based HF medications. We compared prediction performance metrics (accuracy, false positive rate, false negative rate), using rate-ratios (RRs), between subgroups with and without known HF care disparities: below vs above median neighborhood-level socioeconomic status (nSES) and Black vs White race. Results were then applied to 3 hypothetical clinical use cases. RESULTS:Among 34 697 patients (13% Black, 10% Hispanic, 65% White), algorithm accuracy was similar across nSES and racial subgroups. The algorithm assigned more false positives for medication nonadherence among low vs high nSES (RR [95%CI] 1.50 [1.44-1.56]) and Black vs White (2.05 [1.92-2.19]) subgroups. The algorithm also assigned fewer false negatives (0.63 [0.59-0.67]) to Black vs White subgroups. When applied to 3 hypothetical use cases, worsening of existing disparities was pertinent for clinical applications where false positives could be particularly harmful (e.g, if predictions of nonadherence prompted lower treatment priority). DISCUSSION/CONCLUSIONS:Although accuracy was similar across demographic groups, differences in false positive and false negative rates revealed that the same prediction may worsen disparities in some use cases, but not others. CONCLUSION/CONCLUSIONS:Evaluation of predictions in the context of clinical use is essential to avoid unintentionally worsening inequities.

BACKGROUND:Ketamine is increasingly prescribed in an off-label manner to treat psychiatric disorders, raising concerns about direct-to-consumer advertising and the proliferation of clinics offering ketamine for at-home use without direct medical supervision. OBJECTIVES/OBJECTIVE:We aimed to identify and characterize clinics advertising ketamine for psychiatric conditions online in the New York metropolitan area (New York, New Jersey, and Connecticut), with attention to advertising suggesting ketamine is prescribed for at-home use. METHODS:In 2025, systematic web searches were conducted to identify clinics advertising prescription ketamine for psychiatric indications. Public-facing website content was reviewed to describe clinic characteristics: service delivery modality, clinician credentials, routes of administration, disorders treated, and advertising practices. A generalized linear model was used to delineate correlates of clinics advertising ketamine for at-home use. RESULTS:233 clinics were located; 36.5% prescribed ketamine for at-home use. 51.5% listed a medical doctor as part of their team and 42.9% advertised oral ketamine. Depression was the most commonly listed disorder treated (94.0%) and 21.9% advertised ketamine to treat substance use disorder. In the multivariable model, advertising ketamine for at-home use was more common among clinics advertising oral ketamine (aPR = 4.10, 95% CI: 2.20-7.60) and less common among clinics listing a medical doctor (aPR = 0.54, 95% CI: 0.30-0.99). CONCLUSIONS:Over a third of clinics advertised ketamine for at-home use. A limitation is that we only focused on public-facing websites. Advertising practices and clinician representation suggest clinics may be advertising in a more consumer-oriented manner, underscoring the need for monitoring and clearer guidance to mitigate potential safety risks.