Heterogeneous Treatment Effects Research Articles

Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study.

The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention. The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n=850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects. Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025. Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge. ClinicalTrials.Gov identifier: NCT05272176 (https://www. gov/ct2/show/NCT05272176).

Contamination Bias in Linear Regressions

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show these regressions generally fail to estimate convex averages of heterogeneous treatment effects—instead, estimates of each treatment’s effect are contaminated by nonconvex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A reanalysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores. (JEL C21, C31, C51, H75, I21, I28)

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects.

With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with different characteristics are always heterogeneous, and therefore, various heterogeneous treatment effect machine learning estimation methods have been proposed owing to their flexibility and high estimation accuracy. However, most machine learning methods rely on black-box models, preventing direct interpretation of the relationship between patient characteristics and treatment effects. Moreover, most of these studies have focused on continuous or binary outcomes, although survival outcomes are also important in medical research. To address these challenges, we propose a heterogeneous treatment effect estimation method for survival data based on RuleFit, an interpretable machine learning method. Numerical simulation results confirmed that the prediction performance of the proposed method was comparable to that of existing methods. We also applied a dataset from an HIV study, the AIDS Clinical Trials Group Protocol 175 dataset, to illustrate the interpretability of the proposed method using real data. Consequently, the proposed survival causal rule ensemble method provides an interpretable model with sufficient estimation accuracy.

Beyond the Average: Machine Learning for Personalized Causal Inference in Econometrics

In the realm of econometrics, the estimation of average treatment effects (ATE) has traditionally dominated causal inference, often oversimplifying the complex, heterogeneous nature of individual responses to interventions. This study introduces a nuanced approach, "Beyond the Average: Personalized Causal Inference in Econometrics with Machine Learning, " which leverages advanced machine learning (ML) algorithms to shift the focus towards personalized causal effects (PCE), thereby uncovering the variability in treatment effects across individuals. Utilizing a synthetic dataset designed to reflect realistic economic behaviors and responses, we employed Gradient Boosting Machines (GBM) and Causal Forests among other ML techniques to estimate conditional average treatment effects (CATE), providing insights into the heterogeneity of treatment impacts. Our methodology encompassed comprehensive data preprocessing, feature selection based on economic theory and ML insights, and rigorous model validation processes. The results reveal significant heterogeneity in treatment effects, challenging the conventional reliance on ATE and highlighting the importance of considering individual characteristics in policy design and evaluation. Specifically, younger individuals and those with lower income and education levels exhibited markedly different responses to the financial literacy intervention, suggesting that personalized approaches could significantly enhance the effectiveness of such programs. This study not only demonstrates the feasibility and value of applying ML to econometric analysis for personalized causal inference but also lays the groundwork for future research aimed at integrating these methodologies into practical policy - making. By moving beyond the average and embracing the complexity of individual differences, econometric analysis can offer more targeted, effective, and equitable solutions to societal challenges.

Evaluating effects of meal delivery on the ability of homebound older adults to remain in the community via a pragmatic, two-arm, randomized comparative effectiveness trial: study protocol for the Deliver-EE trial

BackgroundAs food insecurity and healthcare costs are linked, healthcare entities (i.e., providers, healthcare systems, insurers) are increasingly interested in identifying and providing solutions to address food insecurity among their patients. Home-delivered meals are one long-standing solution to address food insecurity among homebound older adults. However, there is limited evidence about what mode of delivery is most effective in promoting community independence, reducing healthcare utilization, and improving quality of life as well as how these outcomes may vary as a function of people’s preferences for how meals are delivered to them.MethodsWith extensive stakeholder input, we designed and implemented a pragmatic randomized comparative effectiveness study in which we will enroll 2300 older adults on waiting lists at home-delivered meals programs across the country and randomize them to receive for 6 months, either (1) weekday lunchtime meals delivered by a local volunteer or driver who also provides socialization and wellness checks or (2) biweekly delivery of 10 frozen meals to participants’ homes. Participant data will be combined with Centers for Medicare and Medicaid Services (CMS) data to calculate post-randomization institutional vs. community days. Baseline and 3-month surveys will evaluate secondary outcomes (e.g., food insecurity, loneliness, quality of life) and exploratory outcomes (e.g., nutritional risk). To examine heterogeneity of treatment effects, we will test for interactions between the two types of meal delivery and participants’ preferred mode of meal delivery as well as participants’ living arrangements.DiscussionThis research will be the first to prospectively evaluate the comparative effectiveness of the two predominant meal delivery options. The knowledge generated from this research will be of value to healthcare providers, health systems, payers, community-based organizations, older adults, and their families, because it will identify the mode of meal delivery that best meets homebound older adults’ needs and promotes community independence. In addition, the experience of working closely with stakeholders in designing and conducting this trial will be useful to researchers seeking to engage with stakeholders in the development and evaluation of complex social service interventions while balancing regulatory, resource, and human subjects research considerations.Trial registrationClinicalTrials.gov. NCT05357261. Registered on May 02, 2022

Past, Present and Future of Phase 3 Vaccine Trial Design: Rethinking Statistics for the 21st Century.

Vaccines are crucial for protecting health globally; however, their widespread use relies on rigorous clinical development programmes. This includes Phase 3 randomised controlled trials (RCTs) to confirm their safety, immunogenicity, and efficacy. Traditionally, such trials used fixed designs with predetermined assumptions, lacking the flexibility to change during the trial or stop early due to overwhelming evidence of either efficacy or futility. Modern vaccine trials benefit from innovative approaches like adaptive designs, allowing for planned trial adaptations based on accumulating data. Here, we provide an overview of the evolution of Phase 3 vaccine trial design and statistical analysis methods from traditional to more innovative contemporary methods. This includes adaptive trial designs, which offer ethical advantages and enable early termination if indicated; Bayesian methods, which combine prior knowledge and observed trial data to increase efficiency and enhance result interpretation; modern statistical analysis methods, which enable more accurate and precise inferences; the estimand framework, which ensures the primary question of interest is addressed in a trial; novel approaches using machine learning methods to assess heterogeneity of treatment effects; and statistical advances in safety analysis to evaluate reactogenicity and clinical adverse events. We conclude with insights into the future direction of vaccine trials, aiming to inform clinicians and researchers about conventional and novel RCT design and analysis approaches to facilitate the conduct of efficient, timely trials.

Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps

Population health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.

Blood Pressure Targets for Adults with Vasodilatory Shock - An Individual Patient Data Meta-Analysis.

We sought to estimate whether a lower mean arterial blood pressure target, compared with a higher mean arterial blood pressure target, reduced 90-day all-cause mortality among critically ill adult patients with vasodilatory shock. We conducted an individual patient data meta-analysis of randomized controlled trials that evaluated the effect of distinct thresholds of mean arterial blood pressure to guide vasopressor support among critically ill adults identified in a systematic literature search. The main exposure was a lower mean arterial pressure target compared with a higher mean arterial pressure target (including usual care). The primary outcome was 90-day all-cause mortality. We used a Bayesian random effects log-binomial model to estimate risk ratios with 95% credible intervals (CrIs). Between 2010 and 2019, 3352 patients were randomly assigned in three trials (SEPSISPAM, OVATION pilot trial, and 65-Trial) across 103 hospitals from the United Kingdom, France, and Canada. When compared with a higher mean arterial blood pressure target or usual care, the risk ratio for 90-day all-cause mortality associated with a lower blood pressure target was 0.93 (95% CrI, 0.76 to 1.07; low certainty, posterior probability of benefit 87%). Results were consistent across multiple secondary and sensitivity analyses, including adjustment for prognostically important baseline covariates and alternative modeling techniques. Multiple approaches to evaluate the heterogeneity of treatment effect did not identify any subgroups that may potentially benefit from higher mean arterial blood pressure targets. Targeting a lower mean arterial blood pressure for vasopressor therapy in critically ill patients with vasodilatory shock possibly reduced 90-day all-cause mortality. However, the certainty of evidence is low, and this analysis does not exclude the possibility that lower targets may cause harm overall.

A machine learning approach to unravel client and program-specific effects in opioid treatment retention

This study examines the impact of workforce diversity, particularly the presence of Black/African American staff, on client retention in opioid use disorder (OUD) treatment, recognizing the historically low retention rates among Black and Hispanic populations in such programs. Using a novel machine learning technique called “causal forest,” we explored the heterogeneous treatment effects of staff diversity on client retention, aiming to identify strategies that enhance client retention and improve treatment outcomes. Analyzing data from four waves of the National Drug Abuse Treatment System Survey spanning the years 2000, 2005, 2014, and 2017 (n = 627), we focus on the relationship between workforce diversity and retention. The findings revealed diversity-related variations in retention across 61 out of 627 OUD treatment programs (<10%), with potential beneficial effects attenuated by other program characteristics. These characteristics include programs that are more likely to be private-for-profit, have lower percentages of Black and Latino clients, lower staff-to-client ratios, higher proportions of staff with graduate degrees, and lower percentages of unemployed clients. Our results suggest that workforce diversity alone is insufficient for improving retention. Programs with characteristics linked to greater retention are better positioned to leverage a diverse workforce to enhance retention, offering important implications for policy and program design to better support Black clients with OUDs.

The Heterogeneous Effects of College Education on Outcomes Related to Deaths of Despair.

College education features prominently in research on determinants of deaths from substance use disorders and self-harm-outcomes collectively referred to as "deaths of despair" (DoD). Limited attention has been given to whether the protective effects of college education on indicators of despair vary by individuals' likelihood of college completion. We use data from the National Longitudinal Study of Adolescent to Adult Health for 6,145 individuals to test whether the protective effects of college completion on precursors to DoD vary according to individuals' propensity to attain a college degree. Understanding whether the benefits of college education differ depending on the propensity to complete it is important for designing effective educational policies. Using the heterogeneous treatment effects approach, we find that individuals with a relatively low propensity for graduating from college but who complete it have a lower likelihood of depressive symptoms, binge drinking, prescription drug abuse, and hard drug use.

Abstract 4118122: Effect of Beta Blockers on Exercise Capacity, Diastolic Function, and Quality of Life in Patients with Heart Failure with Preserved Ejection Fraction

Introduction: In recent landmark HFpEF trials, 75-80% of patients are taking beta blockers (BB) without clear evidence of clinical or symptomatic benefit. Patients with HFpEF commonly have comorbidities that have been classically managed with BB (AF, CAD, HTN), though the utility of this medication class for these conditions is being reconsidered. It remains unclear what impact BB use, in patients with HFpEF, has on exercise capacity, diastolic function, and quality of life. Hypothesis: We hypothesized that BB use is associated with worse exercise capacity, diastolic function, and quality of life in HFpEF patients. Methods: This retrospective cohort study was completed using deidentified patient data from the INDIE-HFpEF and RELAX clinical trials (two trials of HFpEF patients). Patients were divided into two groups based on BB use at trial enrollment. The primary outcomes were exercise capacity, diastolic function, and quality of life at baseline evaluation. A multivariate logistic regression model was used to assess for statistically significant differences after controlling for potentially confounding variables including age, sex, BMI, AF, prior MI, COPD, DM, HTN, serum creatinine, and NYHA functional class. Results: A total of 320 patients were included in this analysis, with 230 on BB (72%). Baseline demographics are shown in Table 1. Patients studied were older (68 years old), white (90%), obese (average BMI 34 kg/m 2 ) individuals with an average LVEF of 61%. Outcomes and multivariate linear regression results are shown in Table 2. After controlling for potential confounding variables, there were no differences in baseline exercise capacity [peak VO2 (mL/kg/min): 12.1 vs 13.2, P=0.233], diastolic function [average E/e’: 14 vs 12, P=0.125; LAVI (mL/m 2 ): 42 vs 37, P = 0.665; peak TR velocity (m/s): 2.8 vs 2.7, P = 0.165; PASP (mmHg): 41 vs 36, P = 0.357], quality of life [KCCQ: 54 vs 58, P = 0.206; MLHFQ 43 vs 46, P = 0.762], or NT-proBNP (633 vs 342; pg/mL, P = 0.208) in HFpEF patients on BB compared to those not on BB. Conclusion: There was no association with BB use and worse exercise capacity, diastolic function, or quality of life in HFpEF patients. This may be due to heterogenous treatment effects, with differing impact of BB based upon the underlying pathophysiology of unique HFpEF phenotypes. To definitively determine the impact of BB use in patients with HFpEF, both on clinical outcomes and symptoms, randomized controlled trials are necessary.

Heterogeneous Treatment Effects of High-Frequency Oscillatory Ventilation for Acute Respiratory Distress Syndrome: A Post Hoc Analysis of the Oscillation for Acute Respiratory Distress Syndrome Treated Early (OSCILLATE) Trial.

We sought to evaluate whether different subgroups of adults with acute respiratory distress syndrome (ARDS) respond differently to high-frequency oscillatory ventilation (HFOV). The Oscillation for ARDS Treated Early (OSCILLATE) trial was a randomized controlled trial of HFOV vs. conventional ventilation that found an increased risk of in-hospital mortality (primary outcome) with HFOV. In a post hoc analysis, we applied three different approaches to evaluate heterogeneity of treatment effect for in-hospital mortality: 1) subgroup analyses based on baseline Pao2:Fio2 ratio and oxygenation index (OI); 2) a risk-based approach using a multivariable outcome prediction model; and 3) a clustering approach via multivariable latent class analysis. We used multivariable logistic regression models to assess for interaction. Thirty-nine ICUs, five countries. Five hundred forty-eight adults with moderate to severe ARDS. HFOV vs. conventional mechanical ventilation with low tidal volume and higher positive end-expiratory pressure. The effect of HFOV on in-hospital mortality was consistent across categories of Pao2:Fio2 ratio (adjusted odds ratio [aOR], 2.04; 95% CI, 1.32-3.17 and aOR, 1.16; 95% CI, 0.49-2.75 for groups with Pao2:Fio2 above or equal to 80, vs. below 80, respectively; interaction p = 0.23) and OI (aOR, 1.78; 95% CI, 0.67-4.70; aOR, 3.19; 95% CI, 1.44-7.09; aOR, 1.73; 95% CI, 0.82-3.65; and aOR, 1.33; 95% CI, 0.61-2.90 for quartiles of baseline OI, respectively; interaction p = 0.44). Point estimates for the effect of HFOV were consistent across risk categories (aOR, 2.44; 95% CI, 0.40-14.83; aOR, 1.69; 95% CI, 0.75-3.85; and aOR, 2.10; 95% CI, 0.59-7.54 for the lowest, moderate, and highest risk categories, respectively; interaction p = 0.32). Using a clustering approach, point estimates for HFOV were also consistent (cluster 1: aOR, 1.85; 95% CI, 1.15-3.00 and cluster 2: aOR, 1.75; 95% CI, 0.91-3.38; interaction p = 0.75). We did not identify heterogeneity in the effect of HFOV across different subgroups of patients with ARDS.

Isotonic subgroup selection

Abstract Given a sample of covariate–response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a predetermined threshold. We introduce a computationally feasible approach for subgroup selection in the context of multivariate isotonic regression based on martingale tests and multiple testing procedures for logically structured hypotheses. Our proposed procedure satisfies a non-asymptotic, uniform Type I error rate guarantee with power that attains the minimax optimal rate up to poly-logarithmic factors. Extensions cover classification, isotonic quantile regression, and heterogeneous treatment effect settings. Numerical studies on both simulated and real data confirm the practical effectiveness of our proposal, which is implemented in the R package ISS.

Exploring heterogeneity of treatment effect in patients with sepsis: Protocol for a scoping review.

The average treatment effect (ATE) reported by most randomised clinical trials provides estimates of treatment effects for the theoretical, non-existent average patient. However, ATE may not accurately reflect the outcomes for all subsets of the trial population; some individuals may benefit from the intervention, while others experience worse outcomes or no effect at all. Heterogeneity of treatment effect (HTE) is the non-random and explainable variation in the magnitude or direction of a treatment effect among individuals within a population. Predictive approaches to HTE seek to provide estimates of which treatment of choice is better suited for the individual patient, using regression and/or machine learning techniques. This scoping review aims to investigate the extent to which such predictive approaches to HTE are applied to data from trials on sepsis or septic shock as well as the results of these analyses. The planned review will be conducted in accordance with the PRISMA extension for scoping reviews. We will search Medline, EMBASE, Central, Cinahl and Google Scholar for studies on sepsis or septic shock in which HTE was analysed using predictive approaches. We plan to chart data regarding trial characteristics, patient demographics, disease severity, interventions, outcomes of interest and ATEs, type of predictive approach for the HTE analysis, results from HTE analysis and whether HTE analysis would change an ATE-based trial conclusion. Studies included in the scoping review will be presented as narrative summaries, supplemented with descriptive statistics of quantitative data. The planned scoping review will systematically investigate, summarise and delineate the existing evidence of analysis of HTE in trials on sepsis or septic shock patients as well as their findings, when performed using predictive approaches.

Treatment Effect Heterogeneity in Acute Kidney Injury Incidence Following Intravenous Antihypertensive Administration for Severe Blood Pressure Elevation During Hospitalization

Severe hypertension (sHTN) that develops after hospital admission is prevalent in 10% of patients admitted for reasons other than HTN. sHTN is commonly treated with intravenous (IV) antihypertensives and is associated with a greater risk of acute kidney injury (AKI). Our goal was to explore whether there is heterogeneity in IV antihypertensives' effect on AKI incidence among patients who develop sHTN during hospitalization. Heterogeneity of treatment effect analysis. Patients who developed sHTN, systolic blood pressure (BP) >180, or diastolic BP >110 mmHg during hospitalization and did not have kidney failure. Treatment with IV antihypertensives within 3 hours of BP elevation. Time to develop AKI. An accelerated failure time Bayesian Additive Regression Trees (BART) model to capture the association between the time to develop AKI and predictors. Individual treatment effects estimated for each participant using a counterfactual outcome framework and these estimates were used to identify patient characteristics associated with treatment effect heterogeneity in response to IV antihypertensives. We included 11,951 patients who developed sHTN, 741 were treated with IV antihypertensives, and 11,210 were not, of which 18% and 13% developed AKI, respectively. Most patients would have been harmed from IV antihypertensive treatment except for a small subset of 317 patients who were White, had an SBP on admission ≥156 mmHg, an eGFR ≥70.7 ml/min/1.73m2, and a serum bicarbonate <21.7 mmol/L. Data-driven, hypothesis-generating approach. Findings were not validated with external data sources. These exploratory findings suggest that most patients who develop sHTN will not benefit from IV antihypertensive treatment. Future studies should assess for heterogeneity when identifying treatment options, if any are needed, for sHTN.

Tele-collaborative outreach to rural patients with chronic pain: pragmatic effectiveness trial protocol for the CORPs study.

Despite the increased availability of evidence-based treatments for chronic pain, many patients in rural areas experience poor access to services. Patients receiving care through the VA may also need to navigate multiple systems of care. To examine the effectiveness of a remotely delivered collaborative care intervention for improving pain interference among veterans with high-impact chronic pain living in rural areas. We will conduct a four-site pragmatic effectiveness trial of remotely delivered collaborative care for high-impact chronic pain. Participants (n = 608) will be randomized to the Tele-Collaborative Outreach to Rural Patients (CORPs) intervention or to minimally enhanced usual care (MEUC). Participants randomized to CORPs will complete a biopsychosocial assessment and five follow-up sessions with a nurse care manager (NCM), who will collaborate with a consulting clinician to provide personalized recommendations and care management. CORP participants will also be invited to a virtual 6-session pain education group class. Participants randomized to MEUC will receive a one-time education session with the NCM to review available pain services. All participants will complete quarterly research assessments for one year. The primary study outcome is pain interference. This trial will oversample veterans of female birth sex and minoritized race or ethnicity to test heterogeneity of treatment effects across these patient characteristics. We will conduct an implementation process evaluation and incremental cost-effectiveness analysis. This pragmatic trial will test the real-world effectiveness of a remotely delivered collaborative care intervention for chronic pain. Study findings will inform future implementation efforts to support potential uptake of the intervention.

Discussion to: Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine-learning causal forest analysis

Discussion to: Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine-learning causal forest analysis

Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias

BackgroundCluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer’s and related dementias (ADRD).MethodData from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity.ResultsThe unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA.ConclusionsWe presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.

Safer and targeted use of antipsychotics in youth: an embedded, pragmatic randomized trial

BackgroundAntipsychotic medications (AP) are inappropriately prescribed to young people. The goal of this pragmatic trial was to test a four‐component approach to improved targeting of antipsychotic prescribing to people aged ≥3 and &lt;18 years.MethodsClinicians in four health systems were cluster randomized by the number of previous AP orders and service line – specialty mental health and all others. Intervention arm clinicians received a best practice alert and child psychiatrist consultation and feedback. Families received system navigation and expedited access to psychotherapy. Primary outcomes were total days' supply of AP medication and proportion of youth with any AP supply at 6 months. We estimated the log‐odds of AP use at 6 months and the relative rate of AP over 6 months. The Safer and Targeted Use of Antipsychotics in Youth (SUAY) trial took place between 3/2018 and 12/2020.ResultsThe trial enrolled 733 patients. The odds ratio (OR) comparing use at 6 months was 0.75 (95% CI: 0.52, 1.09). The mean number of days using AP was 118.5 for intervention patients and 128.2 for control patients (relative risk [RR] = 0.92; 95% CI: 0.81–1.04). Exploratory heterogeneity of treatment effects (HTE) was not detected in groups defined by age, gender, provider specialty, and insurance type. HTE by race/ethnicity was present: among youth of color, mean days' supply was 103.2 for intervention arm and 131.2 for the control arm (RR 0.79, 95% CI: 0.67–0.93). Among secondary outcomes, only new psychotherapy referrals differed with 44.3% (n = 154) of intervention participants having a new order for psychotherapy compared to 33.5% (n = 129) in the control arm (OR 1.47: 95% CI: 1.01–2.14).ConclusionsThis intervention did not result in less AP use at 6 months or a reduction in the days' supply of AP medication, although psychotherapy orders increased. The intervention may be effective for some subgroups.

Assessment of treatment effect heterogeneity for multiregional randomized clinical trials

Multiregional clinical trials (MRCTs) have become increasingly common in recent years. Detecting underlying regional heterogeneity is a critical issue for these trials. Existing methods for assessing treatment effect heterogeneity across regions have ignored the incomparability of baseline extrinsic risk factors of the randomized patients from different regions. In this paper, a calibration weighting method is proposed to calibrate the distribution of these extrinsic risk factors between multiple regions. We establish the consistency and the asymptotic normality of the calibration weighting estimator. Simulation studies confirm the finite sample properties of the proposed estimator as well as its superior performance over naive methods and the inverse probability weighting method. The proposed method is illustrated using a randomized clinical trial of adjuvant chemotherapy for resected non-small-cell lung cancer.