Comparing Machine Learning Methods for Estimating Heterogeneous Treatment Effects by Combining Data from Multiple Randomized Controlled Trials

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 improve the power to estimate heterogeneous treatment effects. This paper discusses several non-parametric 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

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

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

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

Optimal Antithrombotic Regimens for Patients With Atrial Fibrillation Undergoing Percutaneous Coronary Intervention An Updated Network Meta-analysis

Importance: Antithrombotic treatment in patients with atrial fibrillation (AF) and percutaneous coronary intervention (PCI) presents a balancing act with regard to bleeding and ischemic risks. Objectives: To evaluate the safety and efficacy of 4 antithrombotic regimens by conducting an up-to-date network meta-analysis and to identify the optimal treatment for patients with AF undergoing PCI. Data Sources: Online computerized database (MEDLINE). Study Selection: Five randomized studies were included (N = 11542; WOEST, PIONEER AF-PCI, RE-DUAL PCI, AUGUSTUS, ENTRUST-AF PCI). Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used in this network meta-analysis, in which bayesian random-effects models were applied. The data were analyzed from September 9 to 29, 2019. Main Outcomes and Measures: The primary safety outcome was thrombolysis in myocardial infarction (TIMI) major bleeding and the primary efficacy outcome was trial-defined major adverse cardiovascular events (MACE). Results: The total number of participants included in the study was 11 532. The mean age of the participants ranged from 70 to 72 years, 69% to 83% were male, 20% to 26% were female, and the participants were predominantly white (>90%). Compared with vitamin K antagonists (VKA) plus dual antiplatelet therapy (DAPT) (reference), the odds ratios (ORs) (95% credible intervals) for TIMI major bleeding were 0.57 (0.31-1.00) for VKA plus P2Y12 inhibitor, 0.69 (0.40-1.16) for non-VKA oral anticoagulant (NOAC) plus DAPT, and 0.52 (0.35-0.79) for NOAC plus P2Y12 inhibitor. For MACE, using VKA plus DAPT as reference, the ORs (95% credible intervals) were 0.97 (0.64-1.42) for VKA plus P2Y12 inhibitor, 0.95 (0.64-1.39) for NOAC plus DAPT, and 1.03 (0.77-1.38) for NOAC plus P2Y12 inhibitor. Conclusions and Relevance: The findings of this study suggest that an antithrombotic regimen of VKA plus DAPT should generally be avoided, because regimens in which aspirin is discontinued may lead to lower bleeding risk and no difference in antithrombotic effectiveness. The use of a NOAC plus a P2Y12 inhibitor without aspirin may be the most favorable treatment option and the preferred antithrombotic regimen for most patients with AF undergoing PCI

Changes of diaphragmatic excursion and lung compliance during major laparoscopic pelvic surgery: A prospective observational study.

Major laparoscopic pelvic surgery requires steep Trendelenburg position with pneumoperitoneum for a long time. We investigated the effect of Trendelenburg position with pneumoperitoneum on diaphragmatic excursion and lung compliance during major laparoscopic pelvic surgery using M-mode sonography. Twenty patients undergoing elective pelviscopic radical hysterectomy were included in this study. Diaphragmatic excursion was measured at the following time points; after sedation, after intubation, 90 minutes after Trendelenburg position with pneumoperitoneum, and after operation with recovery of muscle relaxation. And lung compliance was measured using anesthetic machine under general anesthesia; after the intubation, 90 minutes after Trendelenburg position with pneumoperitoneum and after operation with recovery of muscle relaxation. In order to detect postoperative pulmonary complication, postoperative chest radiography was checked. Static lung compliance, dynamic lung compliance and diaphragmatic excursion were decreased during operation (P < 0.001, respectively). At the end of the operation with recovery of muscle relaxation, reduced diaphragmatic movement was not recovered as its excursion after sedation (P < 0.001). In conclusion, lung compliance was decreased following transiently decreased diaphragmatic excursion during major laparoscopic pelvic surgery

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

NAMPT (visfatin), a direct target of hypoxia-inducible factor-2α, is an essential catabolic regulator of osteoarthritis

Objective: Hypoxia-inducible factor 2α (HIF-2α), encoded by Epas1, causes osteoarthritic cartilage destruction by regulating the expression of matrix-degrading enzymes. We undertook this study to explore the role of nicotinamide phosphoribosyltransferase (NAMPT or visfatin) in HIF-2α-mediated osteoarthritic cartilage destruction. Methods: The expression of HIF-2α, NAMPT and matrix-degrading enzymes was determined at the mRNA and protein levels in human osteoarthritis (OA) cartilage, mouse experimental OA cartilage and primary cultured mouse chondrocytes. Experimental OA in mice was induced by destabilisation of the medial meniscus (DMM) surgery or intra-articular injection of Ad-Epas1 or Ad-Nampt in wild-type, Epas1+/−, Epas1fl/fl;Col2a1-Cre and Col2a1-Nampt transgenic (TG) mice. Primary cultured mouse chondrocytes were treated with recombinant NAMPT protein or were infected with adenoviruses. Results: We found that the Nampt gene is a direct target of HIF-2α in articular chondrocytes and OA cartilage. NAMPT protein, in turn, increased mRNA levels and activities of MMP3, MMP12 and MMP13 in chondrocytes, an action that was necessary for HIF-2α-induced expression of catabolic enzymes. Gain-of-function studies (intra-articular injection of Ad-Nampt; Col2a1-Nampt TG mice) and loss-of-function studies (intra-articular injection of the NAMPT inhibitor FK866) demonstrated that NAMPT is an essential catabolic regulator of osteoarthritic cartilage destruction caused by HIF-2α or DMM surgery. Conclusions: Our findings indicate that NAMPT, whose corresponding gene is a direct target of HIF-2α, plays an essential catabolic role in OA pathogenesis and acts as a crucial mediator of osteoarthritic cartilage destruction caused by HIF-2α or DMM surgery

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