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.

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.

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.

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.

Direct Oral Anticoagulants Versus Warfarin in Patients With Atrial Fibrillation: Patient-Level Network Meta-Analyses of Randomized Clinical Trials With Interaction Testing by Age and Sex.

BACKGROUND: Direct oral anticoagulants (DOACs) are preferred over warfarin for stroke prevention in atrial fibrillation. Meta-analyses using individual patient data offer substantial advantages over study-level data. METHODS: We used individual patient data from the COMBINE AF (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation) database, which includes all patients randomized in the 4 pivotal trials of DOACs versus warfarin in atrial fibrillation (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]), to perform network meta-analyses using a stratified Cox model with random effects comparing standard-dose DOAC, lower-dose DOAC, and warfarin. Hazard ratios (HRs [95% CIs]) were calculated for efficacy and safety outcomes. Covariate-by-treatment interaction was estimated for categorical covariates and for age as a continuous covariate, stratified by sex. RESULTS: A total of 71 683 patients were included (29 362 on standard-dose DOAC, 13 049 on lower-dose DOAC, and 29 272 on warfarin). Compared with warfarin, standard-dose DOACs were associated with a significantly lower hazard of stroke or systemic embolism (883/29 312 [3.01%] versus 1080/29 229 [3.69%]; HR, 0.81 [95% CI, 0.74-0.89]), death (2276/29 312 [7.76%] versus 2460/29 229 [8.42%]; HR, 0.92 [95% CI, 0.87-0.97]), and intracranial bleeding (184/29 270 [0.63%] versus 409/29 187 [1.40%]; HR, 0.45 [95% CI, 0.37-0.56]), but no statistically different hazard of major bleeding (1479/29 270 [5.05%] versus 1733/29 187 [5.94%]; HR, 0.86 [95% CI, 0.74-1.01]), whereas lower-dose DOACs were associated with no statistically different hazard of stroke or systemic embolism (531/13 049 [3.96%] versus 1080/29 229 [3.69%]; HR, 1.06 [95% CI, 0.95-1.19]) but a lower hazard of intracranial bleeding (55/12 985 [0.42%] versus 409/29 187 [1.40%]; HR, 0.28 [95% CI, 0.21-0.37]), death (1082/13 049 [8.29%] versus 2460/29 229 [8.42%]; HR, 0.90 [95% CI, 0.83-0.97]), and major bleeding (564/12 985 [4.34%] versus 1733/29 187 [5.94%]; HR, 0.63 [95% CI, 0.45-0.88]). Treatment effects for standard- and lower-dose DOACs versus warfarin were consistent across age and sex for stroke or systemic embolism and death, whereas standard-dose DOACs were favored in patients with no history of vitamin K antagonist use (P=0.01) and lower creatinine clearance (P=0.09). For major bleeding, standard-dose DOACs were favored in patients with lower body weight (P=0.02). In the continuous covariate analysis, younger patients derived greater benefits from standard-dose (interaction P=0.02) and lower-dose DOACs (interaction P=0.01) versus warfarin. CONCLUSIONS: Compared with warfarin, DOACs have more favorable efficacy and safety profiles among patients with atrial fibrillation.

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.

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.

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.

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

BACKGROUND: The comparative safety and efficacy of the biologics currently approved for asthma are unclear. OBJECTIVE: We compared the safety and efficacy of mepolizumab, benralizumab, and dupilumab in individuals with severe eosinophilic asthma. METHODS: We performed a systematic review of peer-reviewed literature published 2000 to 2021. We studied Bayesian network meta-analyses of exacerbation rates, prebronchodilator FEV1, the Asthma Control Questionnaire, and serious adverse events in individuals with eosinophilic asthma. RESULTS: Eight randomized clinical trials (n = 6461) were identified. We found in individuals with eosinophils ≥300 cells/µL the following: in reducing exacerbation rates compared to placebo: dupilumab (risk ratio [RR], 0.32; 95% credible interval [CI], 0.23 to 0.45), mepolizumab (RR, 0.37; 95% CI, 0.30 to 0.45), and benralizumab (RR, 0.49; 95% CI, 0.43 to 0.55); in improving FEV1: dupilumab (mean difference in milliliters [MD] 230; 95% CI, 160 to 300), benralizumab (MD, 150; 95% CI, 100 to 200), and mepolizumab (MD, 150; 95% CI, 66 to 220); and in reducing Asthma Control Questionnaire scores: mepolizumab (MD, -0.63; 95% CI, -0.81 to -0.45), dupilumab (MD, -0.48; 95% CI, -0.83 to -0.14), and benralizumab (MD, -0.32; 95% CI, -0.43 to -0.21). In individuals with eosinophils 150-299 cells/µL, benralizumab (RR, 0.62; 95% CI, 0.52 to 0.73) and dupilumab (RR, 0.60; 95% CI, 0.38 to 0.95) were associated with lower exacerbation rates; and only benralizumab (MD, 81; 95% CI, 8 to 150) significantly improved FEV1. These differences were minimal compared to clinically important thresholds. For serious adverse events in the overall population, mepolizumab (odds ratio, 0.67; 95% CI, 0.48 to 0.92) and benralizumab (odds ratio, 0.74; 95% CI, 0.59 to 0.93) were associated with lower odds of a serious adverse event, while dupilumab was not different from placebo (odds ratio, 1.0; 95% CI, 0.74 to 1.4). CONCLUSION: There are minimal differences in the efficacy and safety of mepolizumab, benralizumab, and dupilumab in eosinophilic asthma.

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.

Cost-Effectiveness of Systemic Treatments for Metastatic Castration-Sensitive Prostate Cancer: An Economic Evaluation Based on Network Meta-Analysis.

OBJECTIVES: To assess the cost-effectiveness of systemic treatments for metastatic castration-sensitive prostate cancer from the US healthcare sector perspective with a lifetime horizon. METHODS: We built a partitioned survival model based on a network meta-analysis of 7 clinical trials with 7287 patients aged 36 to 94 years between 2004 and 2018 to predict patient health trajectories by treatment. We tested parameter uncertainties with probabilistic sensitivity analyses. We estimated drug acquisition costs using the Federal Supply Schedule and adopted generic drug prices when available. We measured cost-effectiveness by an incremental cost-effectiveness ratio (ICER). RESULTS: The mean costs were approximately $392 000 with androgen deprivation therapy (ADT) alone and approximately $415 000, $464 000, $597 000, and $959 000 with docetaxel, abiraterone acetate, enzalutamide, and apalutamide, added to ADT, respectively. The mean quality-adjusted life-years (QALYs) were 3.38 with ADT alone and 3.92, 4.76, 3.92, and 5.01 with docetaxel, abiraterone acetate, enzalutamide, and apalutamide, added to ADT, respectively. As add-on therapy to ADT, docetaxel had an ICER of $42 069 per QALY over ADT alone; abiraterone acetate had an ICER of $58 814 per QALY over docetaxel; apalutamide had an ICER of $1 979 676 per QALY over abiraterone acetate; enzalutamide was dominated. At a willingness to pay below $50 000 per QALY, docetaxel plus ADT is likely the most cost-effective treatment; at any willingness to pay between $50 000 and $200 000 per QALY, abiraterone acetate plus ADT is likely the most cost-effective treatment. CONCLUSIONS: These findings underscore the value of abiraterone acetate plus ADT given its relative cost-effectiveness to other systemic treatments for metastatic castration-sensitive prostate cancer.

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.

Effect of Haemophilus influenzae Type b and 13-Valent Pneumococcal Conjugate Vaccines on Childhood Pneumonia Hospitalizations and Deaths in Botswana.

BACKGROUND: Globally, pneumonia is the leading cause of death among children. Few data exist regarding the effect of Haemophilus influenzae type b (Hib) vaccine and 13-valent pneumococcal conjugate vaccine (PCV-13) on the burden of childhood pneumonia in African settings. METHODS: We collected data on children aged 1 to 59 months at 3 hospitals in Botswana. Hib vaccine and PCV-13 were introduced in Botswana in November 2010 and July 2012, respectively. We compared pneumonia hospitalizations and deaths prevaccine (January 2009 to October 2010) with postvaccine (January 2013 to December 2017) using seasonally adjusted, interrupted time-series analyses. RESULTS: We identified 6943 pneumonia hospitalizations and 201 pneumonia deaths. In the prevaccine period, pneumonia hospitalizations and deaths increased by 24% (rate, 1.24; 95% CI, .94-1.64) and 59% (rate, 1.59; 95% CI, .87-2.90) per year, respectively. Vaccine introduction was associated with a 48% (95% CI, 29-62%) decrease in the number of pneumonia hospitalizations and a 50% (95% CI, 1-75%) decrease in the number of pneumonia deaths between the end of the prevaccine period (October 2010) and the beginning of the postvaccine period (January 2013). During the postvaccine period, pneumonia hospitalizations and deaths declined by 6% (rate, .94; 95% CI, .89-.99) and 22% (rate, .78; 95% CI, .67-.92) per year, respectively. CONCLUSIONS: Pneumonia hospitalizations and deaths among children declined sharply following introduction of Hib vaccine and PCV-13 in Botswana. This effect was sustained for more than 5 years after vaccine introduction, supporting the long-term effectiveness of these vaccines in preventing childhood pneumonia in Botswana.

Individual Patient Data from the Pivotal Randomized Controlled Trials of Non-Vitamin K Antagonist Oral Anticoagulants in Patients with Atrial Fibrillation (COMBINE AF): Design and Rationale: From the COMBINE AF (A Collaboration between Multiple institutions to Better Investigate Non-vitamin K antagonist oral anticoagulant use in Atrial Fibrillation) Investigators.

BACKGROUND: Non-vitamin K antagonist oral anticoagulants (NOACs) are the preferred class of medications for prevention of stroke and systemic embolism in patients with atrial fibrillation unless contraindications exist. Five large, international, randomized, controlled trials of NOACs versus either warfarin or aspirin have been completed to date. DESIGN: COMBINE AF incorporates de-identified individual patient data from 77,282 patients with atrial fibrillation at risk for stroke randomized to NOAC, warfarin, or aspirin from 5 pivotal randomized controlled trials. All patients randomized in the constituent trials are included. Variables common to ≥3 of the constituent trials are included in the master database. Individual trial data sets from the 4 coordinating centers were combined at the Duke Clinical Research Institute. The final database will be securely shared with the 4 academic coordinating centers. The combined master database will be used to perform statistical analyses aimed at better understanding underlying risk factors and outcomes in patients with atrial fibrillation treated with oral anticoagulants, with a special focus on patient subgroups and uncommon outcomes. The initial analysis from COMBINE AF will be a network meta-analysis investigating the relative efficacy and safety of pooled higher-dose NOACs versus pooled lower-dose NOACs versus warfarin with respect to multiple time-to-event efficacy and safety outcomes. COMBINE AF is registered with PROSPERO (CRD42020178771). CONCLUSION: In conclusion, COMBINE AF provides a rich and robust database consisting of individual patient data and will offer opportunities to investigate oral anticoagulants across many patient subgroups. Data sharing and collaboration across academic institutions and investigators will serve as overarching themes.

Generalizability of Subgroup Effects.

Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly. However, researchers may be concerned that in fact subgroup-specific effects differ between sample and population. In this brief report, we explore the generalizability of subgroup effects. First, we derive the bias in the sample average treatment effect estimator as an estimate of the population average treatment effect when subgroup effects in the sample do not directly generalize. Next, we present a Monte Carlo simulation to explore bias due to unmeasured heterogeneity of subgroup effects across sample and population. Finally, we examine the potential for bias in an illustrative data example. Understanding the generalizability of subgroup effects may lead to increased use of these methods for making externally valid inferences of treatment effects using a study sample.

Meta-analysis of rare adverse events in randomized clinical trials: Bayesian and frequentist methods.

BACKGROUND/AIMS: Regulatory approval of a drug or device involves an assessment of not only the benefits but also the risks of adverse events associated with the therapeutic agent. Although randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness, the number of treated patients in a single RCT may not be enough to detect a rare but serious side effect of the treatment. Meta-analysis plays an important role in the evaluation of the safety of medical products and has advantage over analyzing a single RCT when estimating the rate of adverse events. METHODS: In this article, we compare 15 widely used meta-analysis models under both Bayesian and frequentist frameworks when outcomes are extremely infrequent or rare. We present extensive simulation study results and then apply these methods to a real meta-analysis that considers RCTs investigating the effect of rosiglitazone on the risks of myocardial infarction and of death from cardiovascular causes. RESULTS: Our simulation studies suggest that the beta hyperprior method modeling treatment group-specific parameters and accounting for heterogeneity performs the best. Most models ignoring between-study heterogeneity give poor coverage probability when such heterogeneity exists. In the data analysis, different methods provide a wide range of log odds ratio estimates between rosiglitazone and control treatments with a mixed conclusion on their statistical significance based on 95% confidence (or credible) intervals. CONCLUSION: In the rare event setting, treatment effect estimates obtained from traditional meta-analytic methods may be biased and provide poor coverage probability. This trend worsens when the data have large between-study heterogeneity. In general, we recommend methods that first estimate the summaries of treatment-specific risks across studies and then relative treatment effects based on the summaries when appropriate. Furthermore, we recommend fitting various methods, comparing the results and model performance, and investigating any significant discrepancies among them.

Propensity Score-Based Estimators With Multiple Error-Prone Covariates.

Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.

Keloid Excision and Adjuvant Treatments: A Network Meta-analysis.

BACKGROUND: Keloid disease treatment continues to be unsatisfactory with high recurrence rates. We evaluated the literature regarding the effectiveness of keloid excision with various adjuvant treatments following surgery and assessed recurrence rates. METHODS: We systematically searched databases through November 2016. We performed pairwise meta-analyses and Bayesian network meta-analyses on the number of recurrences. RESULTS: Following screening, 14 studies including 996 patients with various types of keloids were eligible for inclusion. Patients were categorized based on the receipt of surgery and the type of adjuvant treatment employed afterward. Paired meta-analysis (6 meta-analyses) showed that "excision + 1 adjuvant drug" led to statistically significantly higher odds of recurrence compared to "excision + radiation" (odds ratio [OR], 3.22; 95% confidence interval [CI], 1.35-7.67). Based on the network meta-analyses, the ORs of keloid recurrence following various treatments compared to no excision were as follows: "excision + pressure, 0.18 (95% CI, 0.01-7.07); excision + 2 adjuvants drugs, 0.47 (95% CI, 0.02-12.82); excision + radiation, 0.39 (95% CI, 0.04-3.31); excision + skin grafting, 0.58 (95% CI, 0.00-76.10); excision + 1 adjuvant drug, 1.76 (95% CI, 0.17-21.35); and excision only, 2.17 (95% CI, 0.23-23.95). CONCLUSIONS: According to our results, "excision + radiation" had significantly better outcomes than excision alone. "Excision + pressure" had better outcomes than excision + any other treatment modality, and excision + nonradiation adjuvant therapies were also better than "excision only," although these findings did not reach statistical significance.

Comparative Effectiveness of First-Line Medications for Primary Open-Angle Glaucoma: A Systematic Review and Network Meta-analysis.

TOPIC: Primary open-angle glaucoma (POAG) is a highly prevalent condition worldwide and the most common cause of irreversible sight loss. The objective is to assess the comparative effectiveness of first-line medical treatments in patients with POAG or ocular hypertension through a systematic review and network meta-analysis, and to provide relative rankings of these treatments. CLINICAL RELEVANCE: Treatment for POAG currently relies completely on lowering the intraocular pressure (IOP). Although topical drops, lasers, and surgeries can be considered in the initial treatment of glaucoma, most patients elect to start treatment with eye drops. METHODS: We included randomized controlled trials (RCTs) that compared a single active topical medication with no treatment/placebo or another single topical medication. We searched CENTRAL, MEDLINE, EMBASE, and the Food and Drug Administration's website. Two individuals independently assessed trial eligibility, abstracted data, and assessed the risk of bias. We performed Bayesian network meta-analyses. RESULTS: We included 114 RCTs with data from 20 275 participants. The overall risk of bias of the included trials is mixed. The mean reductions (95% credible intervals) in IOP in millimeters of mercury at 3 months ordered from the most to least effective drugs were as follows: bimatoprost 5.61 (4.94; 6.29), latanoprost 4.85 (4.24; 5.46), travoprost 4.83 (4.12; 5.54), levobunolol 4.51 (3.85; 5.24), tafluprost 4.37 (2.94; 5.83), timolol 3.70 (3.16; 4.24), brimonidine 3.59 (2.89; 4.29), carteolol 3.44 (2.42; 4.46), levobetaxolol 2.56 (1.52; 3.62), apraclonidine 2.52 (0.94; 4.11), dorzolamide 2.49 (1.85; 3.13), brinzolamide 2.42 (1.62; 3.23), betaxolol 2.24 (1.59; 2.88), and unoprostone 1.91 (1.15; 2.67). CONCLUSIONS: All active first-line drugs are effective compared with placebo in reducing IOP at 3 months. Bimatoprost, latanoprost, and travoprost are among the most efficacious drugs, although the within-class differences were small and may not be clinically meaningful. All factors, including adverse effects, patient preferences, and cost, should be considered in selecting a drug for a given patient.

Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment.

Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research.