Obstetric Racial Disparities in the Era of the ARRIVE (A Randomized Trial of Induction Versus Expectant Management) Trial and the Coronavirus Disease 2019 (COVID-19) Pandemic.

OBJECTIVE: To evaluate the influence of the ARRIVE (A Randomized Trial of Induction Versus Expectant Management) trial and the coronavirus disease 2019 (COVID-19) pandemic on racial and ethnic differences in labor induction, pregnancy-associated hypertension, and cesarean delivery among non-Hispanic Black and non-Hispanic White low-risk, first-time pregnancies. METHODS: We conducted an interrupted time series analysis of U.S. birth certificate data from maternal non-Hispanic Black and non-Hispanic White race and ethnicity, first pregnancy, 39 or more weeks of gestation, with no documented contraindication to vaginal delivery or expectant management beyond 39 weeks. We compared the rate of labor induction (primary outcome), pregnancy-associated hypertension, and cesarean delivery during three time periods: pre-ARRIVE (January 1, 2015-July 31, 2018), post-ARRIVE (November 1, 2018-February 29, 2020), and post-COVID-19 (March 1, 2020-December 31, 2021). RESULTS: In the post-ARRIVE period, the rate of labor induction increased in both non-Hispanic White and non-Hispanic Black patients, with no statistically significant difference in the magnitude of increase between the two groups (rate ratio for race [RR race ] 0.98, 95% CI, 0.95-1.02, P =.289). Post-COVID-19, the rate of labor induction increased in non-Hispanic White but not non-Hispanic Black patients. The magnitude of the rate change between non-Hispanic White and non-Hispanic Black patients was significant (RR race 0.95, 95% CI, 0.92-0.99, P =.009). Non-Hispanic Black pregnant people were more likely to have pregnancy-associated hypertension and more often delivered by cesarean at all time periods. CONCLUSION: Changes in obstetric practice after both the ARRIVE trial and the COVID-19 pandemic were not associated with changes in Black-White racial differences in labor induction, cesarean delivery, and pregnancy-associated hypertension.

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.

While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.

Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.

Cardiovascular Effects of GnRH Antagonists Compared With Agonists in Prostate Cancer: A Systematic Review.

Background: Androgen deprivation therapy is the cornerstone of treatment for patients with advanced prostate cancer. Meta-analysis of small, oncology-focused trials suggest gonadotropin-releasing hormone (GnRH) antagonists may be associated with fewer adverse cardiovascular outcomes compared with GnRH agonists. Objectives: This study sought to determine whether GnRH antagonists were associated with fewer major adverse cardiovascular events compared with GnRH agonists. Methods: Electronic databases were searched for all prospective, randomized trials comparing GnRH antagonists with agonists. The primary outcome was a major adverse cardiovascular event as defined by the following standardized Medical Dictionary for Regulatory Activities terms: "myocardial infarction," "central nervous system hemorrhages and cerebrovascular conditions," and all-cause mortality. Bayesian meta-analysis models with random effects were fitted. Results: A total of 11 eligible studies of a maximum duration of 3 to 36 months (median = 12 months) enrolling 4,248 participants were included. Only 1 trial used a blinded, adjudicated event process, whereas potential bias persisted in all trials given their open-label design. A total of 152 patients with primary outcome events were observed, 76 of 2,655 (2.9%) in GnRH antagonist-treated participants and 76 of 1,593 (4.8%) in agonist-treated individuals. Compared with GnRH agonists, the pooled OR of GnRH antagonists for the primary endpoint was 0.57 (95% credible interval: 0.37-0.86) and 0.58 (95% credible interval: 0.32-1.08) for all-cause death. Conclusions: Despite the addition of the largest, dedicated cardiovascular outcome trial, the volume and quality of available data to definitively answer this question remain suboptimal. Notwithstanding these limitations, the available data suggest that GnRH antagonists are associated with fewer cardiovascular events, and possibly mortality, compared with GnRH agonists.

Harms were detected but not reported in six clinical trials of gabapentin.

OBJECTIVES: We sought to assess and report harms that were observed but not disclosed previously in clinical trials of gabapentin. STUDY DESIGN AND SETTING: We reanalyzed individual participant data from six randomized parallel trials of gabapentin for neuropathic pain, and we conducted meta-analyses. Between 1996 and 2003, adult participants were assigned to gabapentin (600 mg-3,600 mg per day) or placebo for 7-14 weeks. We calculated the proportion of participants with: one or more adverse events (AEs), one or more serious AEs, discontinued, discontinued because AEs. We also estimated effects on AEs at three levels of aggregation using COSTART, a hierarchical system for classifying AEs: body system, midlevel system, preferred term. RESULTS: We found evidence of important harms that were neither included in published trial reports nor included in systematic reviews. Aggregating related harms led to greater confidence that gabapentin might harm the nervous system and possibly the digestive, metabolic and nutritional, respiratory, sensory, and urogenital body systems. Nervous system harms were more common than previous reports suggest. CONCLUSION: Clinical trials identified harms that were not reported publicly, and journal articles overstated uncertainty about harms. Relying on journal articles to evaluate gabapentin's harms could contribute to incomplete and misleading conclusions in systematic reviews and guidelines. To prevent bias arising from the selective nonreporting of results, journal articles should describe how to access data for all harms observed in clinical trials (e.g., by sharing individual participant data).

Methodology of a Natural History Study of a Rare Neurodevelopmental Disorder: Alternating Hemiplegia of Childhood as a Prototype Disease.

Here, we describe the process of development of the methodology for an international multicenter natural history study of alternating hemiplegia of childhood as a prototype disease for rare neurodevelopmental disorders. We describe a systematic multistep approach in which we first identified the relevant questions about alternating hemiplegia of childhood natural history and expected challenges. Then, based on our experience with alternating hemiplegia of childhood and on pragmatic literature searches, we identified solutions to determine appropriate methods to address these questions. Specifically, these solutions included development and standardization of alternating hemiplegia of childhood-specific spell video-library, spell calendars, adoption of tailored methodologies for prospective measurement of nonparoxysmal and paroxysmal manifestations, unified data collection protocols, centralized data platform, adoption of specialized analysis methods including, among others, Cohen kappa, interclass correlation coefficient, linear mixed effects models, principal component, propensity score, and ambidirectional analyses. Similar approaches can, potentially, benefit in the study of other rare pediatric neurodevelopmental disorders.

A note on semiparametric efficient generalization of causal effects from randomized trials to target populations.

When effect modifiers influence the decision to participate in randomized trials, generalizing causal effect estimates to an external target population requires the knowledge of two scores - the propensity score for receiving treatment and the sampling score for trial participation. While the former score is known due to randomization, the latter score is usually unknown and estimated from data. Under unconfounded trial participation, we characterize the asymptotic efficiency bounds for estimating two causal estimands - the population average treatment effect and the average treatment effect among the non-participants - and examine the role of the scores. We also study semiparametric efficient estimators that directly balance the weighted trial sample toward the target population, and illustrate their operating characteristics via simulations.

External Control Arms in Idiopathic Pulmonary Fibrosis Using Clinical Trial and Real-World Data Sources.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease for which novel therapies are needed. External controls (ECs) could enhance IPF trial efficiency, but the direct comparability of ECs versus concurrent controls is unknown. Objectives: To develop IPF ECs by fit-for-purpose data standards to historical randomized clinical trial (RCT), multicenter registry (Pulmonary Fibrosis Foundation Patient Registry), and electronic health record (EHR) data and to evaluate endpoint comparability among ECs and the phase II RCT of BMS-986020. Methods: After data curation, the rate of change in FVC from baseline to 26 weeks among participants receiving BMS-986020 600 mg twice daily was compared with the BMS-placebo arm and ECs using mixed-effects models with inverse probability weights. Measurements and Main Results: At 26 weeks, the rates of change in FVC were -32.71 ml for BMS-986020 and -130.09 ml for BMS-placebo (difference, 97.4 ml; 95% confidence interval [CI], 24.6-170.2), replicating the original BMS-986020 RCT. RCT ECs showed treatment effect point estimates within the 95% CI of the original BMS-986020 RCT. Pulmonary Fibrosis Foundation Patient Registry ECs and EHR ECs experienced a slower rate of FVC decline compared with the BMS-placebo arm, resulting in treatment-effect point estimates outside of the 95% CI of the original BMS-986020 RCT. Conclusions: IPF ECs generated from historical RCT placebo arms result in comparable primary treatment effects to that of the original clinical trial, whereas ECs from real-world data sources, including registry or EHR data, do not. RCT ECs may serve as a potentially useful supplement to future IPF RCTs.

Safety and efficacy of hydroxychloroquine as prophylactic against COVID-19 in healthcare workers: a meta-analysis of randomised clinical trials.

OBJECTIVE: We studied the safety and efficacy of hydroxychloroquine (HCQ) as pre-exposure prophylaxis for COVID-19 in healthcare workers (HCWs), using a meta-analysis of randomised controlled trials (RCTs). DATA SOURCES: PubMed and EMBASE databases were searched to identify randomised trials studying HCQ. STUDY SELECTION: Ten RCTs were identified (n=5079 participants). DATA EXTRACTION AND SYNTHESIS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in this systematic review and meta-analysis between HCQ and placebo using a Bayesian random-effects model. A pre-hoc statistical analysis plan was written. MAIN OUTCOMES: The primary efficacy outcome was PCR-confirmed SARS-CoV-2 infection and the primary safety outcome was incidence of adverse events. The secondary outcome included clinically suspected SARS-CoV-2 infection. RESULTS: Compared with placebo, HCWs randomised to HCQ had no significant difference in PCR-confirmed SARS-CoV-2 infection (OR 0.92, 95% credible interval (CI): 0.58, 1.37) or clinically suspected SARS-CoV-2 infection (OR 0.78, 95% CI: 0.57, 1.10), but significant difference in adverse events (OR 1.35, 95% CI: 1.03, 1.73). CONCLUSIONS AND RELEVANCE: Our meta-analysis of 10 RCTs investigating the safety and efficacy of HCQ as pre-exposure prophylaxis in HCWs found that compared with placebo, HCQ does not significantly reduce the risk of confirmed or clinically suspected SARS-CoV-2 infection, while HCQ significantly increases adverse events. PROSPERO REGISTRATION NUMBER: CRD42021285093.

Calibrated meta-analysis to estimate the efficacy of mental health treatments in target populations: an application to paliperidone trials for treatment of schizophrenia.

BACKGROUNDS: Meta-analyses can be a powerful tool but need to calibrate potential unrepresentativeness of the included trials to a target population. Estimating target population average treatment effects (TATE) in meta-analyses is important to understand how treatments perform in well-defined target populations. This study estimated TATE of paliperidone palmitate in patients with schizophrenia using meta-analysis with individual patient trial data and target population data. METHODS: We conducted a meta-analysis with data from four randomized clinical trials and target population data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Efficacy was measured using the Positive and Negative Syndrome Scale (PANSS). Weights to equate the trial participants and target population were calculated by comparing baseline characteristics between the trials and CATIE. A calibrated weighted meta-analysis with random effects was performed to estimate the TATE of paliperidone compared to placebo. RESULTS: A total of 1,738 patients were included in the meta-analysis along with 1,458 patients in CATIE. After weighting, the covariate distributions of the trial participants and target population were similar. Compared to placebo, paliperidone palmitate was associated with a significant reduction of the PANSS total score under both unweighted (mean difference 9.07 [4.43, 13.71]) and calibrated weighted (mean difference 6.15 [2.22, 10.08]) meta-analysis. CONCLUSIONS: The effect of paliperidone palmitate compared with placebo is slightly smaller in the target population than that estimated directly from the unweighted meta-analysis. Representativeness of samples of trials included in a meta-analysis to a target population should be assessed and incorporated properly to obtain the most reliable evidence of treatment effects in target populations.

Direct Oral Anticoagulants Versus Warfarin Across the Spectrum of Kidney Function: Patient-Level Network Meta-Analyses From COMBINE AF.

BACKGROUND: There is uncertainty surrounding the use of direct oral anticoagulants (DOACs) in patients with kidney dysfunction. METHODS: Using the COMBINE AF (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation) database (data from RE-LY [Randomized Evaluation of Long-term Anticoagulation Therapy], ROCKET AF [Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation], ARISTOTLE [Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation], and ENGAGE AF-TIMI 48 [Effective Anticoagulation With Factor Xa Next Generation in Atrial Fibrillation-Thrombolysis in Myocardial Infarction 48]), we performed an individual patient-level network meta-analysis to evaluate the safety and efficacy of DOACs versus warfarin across continuous creatinine clearance (CrCl). A multivariable Cox model including treatment-by-CrCl interaction with random effects was fitted to estimate hazard ratios for paired treatment strategies (standard-dose DOAC, lower-dose DOAC, and warfarin). Outcomes included stroke and systemic embolism (S/SE), major bleeding, intracranial hemorrhage (ICH), and death. RESULTS: Among 71 683 patients (mean age, 70.6±9.4 years; 37.3% female; median follow-up, 23.1 months), the mean CrCl was 75.5±30.5 mL/min. The incidence of S/SE, major bleeding, ICH, and death increased significantly with worsening kidney function. Across continuous CrCl values down to 25 mL/min, the hazard of major bleeding did not change for patients randomized to standard-dose DOACs compared with those randomized to warfarin (Pinteraction=0.61). Compared with warfarin, standard-dose DOAC use resulted in a significantly lower hazard of ICH at CrCl values <122 mL/min, with a trend for increased safety with DOAC as CrCl decreased (6.2% decrease in hazard ratio per 10-mL/min decrease in CrCl; Pinteraction=0.08). Compared with warfarin, standard-dose DOAC use resulted in a significantly lower hazard of S/SE with CrCl <87 mL/min, with a significant treatment-by-CrCl effect (4.8% decrease in hazard ratio per 10-mL/min decrease in CrCl; Pinteraction=0.01). The hazard of death was significantly lower with standard-dose DOACs for patients with CrCl <77 mL/min, with a trend toward increasing benefit with lower CrCl (2.1% decrease in hazard ratio per 10-mL/min decrease in CrCl; Pinteraction=0.08). Use of lower-dose rather than standard-dose DOACs was not associated with a significant difference in incident bleeding or ICH in patients with reduced kidney function but was associated with a higher incidence4 of death and S/SE. CONCLUSIONS: Standard-dose DOACs are safer and more effective than warfarin down to a CrCl of at least 25 mL/min. Lower-dose DOACs do not significantly lower the incidence of bleeding or ICH compared with standard-dose DOACs but are associated with a higher incidence of S/SE and death. These findings support the use of standard-dose DOACs over warfarin in patients with kidney dysfunction.

The normality assumption on between-study random effects was questionable in a considerable number of Cochrane meta-analyses.

BACKGROUND: Studies included in a meta-analysis are often heterogeneous. The traditional random-effects models assume their true effects to follow a normal distribution, while it is unclear if this critical assumption is practical. Violations of this between-study normality assumption could lead to problematic meta-analytical conclusions. We aimed to empirically examine if this assumption is valid in published meta-analyses. METHODS: In this cross-sectional study, we collected meta-analyses available in the Cochrane Library with at least 10 studies and with between-study variance estimates > 0. For each extracted meta-analysis, we performed the Shapiro-Wilk (SW) test to quantitatively assess the between-study normality assumption. For binary outcomes, we assessed between-study normality for odds ratios (ORs), relative risks (RRs), and risk differences (RDs). Subgroup analyses based on sample sizes and event rates were used to rule out the potential confounders. In addition, we obtained the quantile-quantile (Q-Q) plot of study-specific standardized residuals for visually assessing between-study normality. RESULTS: Based on 4234 eligible meta-analyses with binary outcomes and 3433 with non-binary outcomes, the proportion of meta-analyses that had statistically significant non-normality varied from 15.1 to 26.2%. RDs and non-binary outcomes led to more frequent non-normality issues than ORs and RRs. For binary outcomes, the between-study non-normality was more frequently found in meta-analyses with larger sample sizes and event rates away from 0 and 100%. The agreements of assessing the normality between two independent researchers based on Q-Q plots were fair or moderate. CONCLUSIONS: The between-study normality assumption is commonly violated in Cochrane meta-analyses. This assumption should be routinely assessed when performing a meta-analysis. When it may not hold, alternative meta-analysis methods that do not make this assumption should be considered.

Racial Inequities in Sepsis Mortality Among Children in the United States.

BACKGROUND: Racial inequities influence health outcomes in the United States, but their impact on sepsis outcomes among children is understudied. We aimed to evaluate for racial inequities in sepsis mortality using a nationally representative sample of pediatric hospitalizations. METHODS: This population-based, retrospective cohort study used the 2006, 2009, 2012 and 2016 Kids' Inpatient Database. Eligible children 1 month to 17 years old were identified using sepsis-related International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision codes. We used modified Poisson regression to evaluate the association between patient race and in-hospital mortality, clustering by hospital and adjusting for age, sex and year. We used Wald tests to assess for modification of associations between race and mortality by sociodemographic factors, geographic region and insurance status. RESULTS: Among 38,234 children with sepsis, 2555 (6.7%) died in-hospital. Compared with White children, mortality was higher among Hispanic (adjusted relative risk: 1.09; 95% confidence interval: 1.05-1.14), Asian/Pacific Islander (1.17, 1.08-1.27) and children from other racial minority groups (1.27, 1.19-1.35). Black children had similar mortality to White children overall (1.02, 0.96-1.07), but higher mortality in the South (7.3% vs. 6.4%; P < 0.0001). Hispanic children had higher mortality than White children in the Midwest (6.9% vs. 5.4%; P < 0.0001), while Asian/Pacific Islander children had higher mortality than all other racial categories in the Midwest (12.6%) and South (12.0%). Mortality was higher among uninsured children than among privately insured children (1.24, 1.17-1.31). CONCLUSIONS: Risk of in-hospital mortality among children with sepsis in the United States differs by patient race, geographic region and insurance status.

Systematic Review and Meta-analysis for Surgery Versus Stereotactic Radiosurgery for Jugular Paragangliomas.

OBJECTIVE: Comprehensively analyze tumor control and treatment complications for jugular paraganglioma patients undergoing surgery versus stereotactic radiosurgery (SRS). DATABASES REVIEWED: EMBASE, Medline, and Scopus. METHODS: The databases were searched for English and Spanish articles from January 1, 1995, to January, 1, 2019, for studies reporting tumor control and treatment side effects regarding patients with jugular paraganglioma treated with surgery or SRS. Main outcome measures included short-term and long-term tumor recurrence, as well as postintervention complications. RESULTS: We identified 10,952 original abstracts, 705 eligible studies, and 107 studies for final data extraction. There were 3,498 patients-2,215 surgical patients and 1,283 SRS patients. Bayesian meta-analysis was applied to the extracted data, with tau measurements for study heterogeneity. SRS tumors were larger (3.9 cm 3 versus 8.1 cm 3 ). Meta-analysis results demonstrated low rates of long-term recurrence for both modalities (surgery, 15%; SRS, 7%), with SRS demonstrating lower rates of postintervention cerebrospinal fluid leak, dysphagia, and cranial nerve Vll, lX, X, Xl, or Xll palsies. CONCLUSIONS: This study demonstrates excellent control of jugular paragangiomas with both surgery and SRS, with higher rates of lower cranial neuropathies, dysphagia, and cerebrospinal fluid leaks among surgical patients.

Comparative efficacy of tezepelumab to mepolizumab, benralizumab, and dupilumab in eosinophilic asthma: A Bayesian network meta-analysis.

BACKGROUND: It is unclear how the efficacy of tezepelumab, approved for the treatment of type 2 high and low asthma, compares to the efficacy of other biologics for type 2-high asthma. OBJECTIVES: We sought to conduct an indirect comparison of tezepelumab to dupilumab, benralizumab, and mepolizumab in the treatment of eosinophilic asthma. METHODS: The investigators conducted a systematic review and Bayesian network meta-analyses. They identified randomized controlled trials indexed in PubMed, Embase, or Cochrane Central Register of Controlled Trials (CENTRAL) between January 1, 2000, and August 12, 2022. Outcomes included exacerbation rates, prebronchodilator FEV1, and the Asthma Control Questionnaire. RESULTS: Ten randomized controlled trials (n = 9201) met eligibility. Tezepelumab (relative risk: 0.63; 95% credible interval [CI]: 0.46-0.86) was associated with significantly lower exacerbation rates than benralizumab and larger improvements in FEV1 compared to mepolizumab (mean difference [MD]: 66; 95% CI: -33 to 170) and benralizumab (MD: 62; 95% CI: -22 to 150), though the 95% CI crossed the null value of 0. Mepolizumab improved the Asthma Control Questionnaire score the most, but this improvement was not significantly different from that of tezepelumab (tezepelumab vs mepolizumab; MD: 0.14; 95% CI: -0.10 to 0.38). For efficacy by clinically important thresholds, tezepelumab, mepolizumab, and dupilumab achieved a >99% probability of reducing exacerbation rates by ≥50% compared to placebo, but benralizumab had only a 66% probability of doing so. Tezepelumab and dupilumab had a probability of 1.00 of improving prebronchodilator FEV1 by ≥100 mL above placebo. Compared to mepolizumab, dupilumab had >90% chance for improving FEV1 by ≥50 mL, but none of the differences between biologics exceeded 100 mL. CONCLUSIONS: In individuals with eosinophilic asthma, tezepelumab and dupilumab were associated with greater improvements (although below clinical thresholds) in exacerbation rates and lung function than benralizumab or mepolizumab.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Comparing the performance of statistical methods that generalize effect estimates from randomized controlled trials to much larger target populations.

Policymakers use results from randomized controlled trials to inform decisions about whether to implement treatments in target populations. Various methods - including inverse probability weighting, outcome modeling, and Targeted Maximum Likelihood Estimation - that use baseline data available in both the trial and target population have been proposed to generalize the trial treatment effect estimate to the target population. Often the target population is significantly larger than the trial sample, which can cause estimation challenges. We conduct simulations to compare the performance of these methods in this setting. We vary the size of the target population, the proportion of the target population selected into the trial, and the complexity of the true selection and outcome models. All methods performed poorly when the trial size was only 2% of the target population size or the target population included only 1,000 units. When the target population or the proportion of units selected into the trial was larger, some methods, such as outcome modeling using Bayesian Additive Regression Trees, performed well. We caution against generalizing using these existing approaches when the target population is much larger than the trial sample and advocate future research strives to improve methods for generalizing to large target populations.

Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials.

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

Landscape of coronavirus disease 2019 clinical trials: New frontiers and challenges.

BACKGROUND/AIM: The number of coronavirus disease 2019 deaths and cases continues to increase globally. Novel therapies are urgently needed to treat patients with coronavirus disease 2019. We sought to provide a critical review of trials designed during the coronavirus disease 2019 pandemic. Our primary goal was to provide a critical review of the landscape of clinical trials designed to address the coronavirus disease 2019 pandemic. Specifically, we were interested in assessing the design of phase II/III and phase III interventional trials. METHODS: We utilized the ClinicalTrials.gov database to include trials registered between 1 December 2019 and 11 April 2021 to survey the current landscape of clinical trials for coronavirus disease 2019. Variables extracted included: National Clinical Trial number, title, location, sponsor, study type, start date, completion date, gender group, age group, primary outcome, secondary outcome, overall status, and associated references. RESULTS: About 57% of studies were interventional, 14.5% were phase III trials, and the majority of the therapeutic trials included hospitalized patients. There were 52 primary composite outcomes and 285 unique interventions spanning 10 drug classes. The outcomes, disease severity, and comparators varied substantially across trials, and the trials were often too small to be definitive. CONCLUSION: These findings are relevant as we strongly advocate for global coordination of efforts through the use of common platforms that enable harmonizing of endpoints, collection of common key variables and clear definition of disease severity to have clinically meaningful results from clinical trials.