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Background: Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. Methods: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. Results: Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting. Conclusion: Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting. [ABSTRACT FROM AUTHOR]

The meta‐analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision‐making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial‐normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non‐informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta‐binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta‐analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies. [ABSTRACT FROM AUTHOR]

Rare events meta‐analyses of randomized controlled trials (RCTs) are often underpowered because the outcomes are infrequent. Real‐world evidence (RWE) from non‐randomized studies may provide valuable complementary evidence about the effects of rare events, and there is growing interest in including such evidence in the decision‐making process. Several methods for combining RCTs and RWE studies have been proposed, but the comparative performance of these methods is not well understood. We describe a simulation study that aims to evaluate an array of alternative Bayesian methods for including RWE in rare events meta‐analysis of RCTs: the naïve data synthesis, the design‐adjusted synthesis, the use of RWE as prior information, the three‐level hierarchical models, and the bias‐corrected meta‐analysis model. The percentage bias, root‐mean‐square‐error, mean 95% credible interval width, coverage probability, and power are used to measure performance. The various methods are illustrated using a systematic review to evaluate the risk of diabetic ketoacidosis among patients using sodium/glucose co‐transporter 2 inhibitors as compared with active‐comparators. Our simulations show that the bias‐corrected meta‐analysis model is comparable to or better than the other methods in terms of all evaluated performance measures and simulation scenarios. Our results also demonstrate that data solely from RCTs may not be sufficiently reliable for assessing the effects of rare events. In summary, the inclusion of RWE could increase the certainty and comprehensiveness of the body of evidence of rare events from RCTs, and the bias‐corrected meta‐analysis model may be preferable. [ABSTRACT FROM AUTHOR]

Background: Many rare events meta-analyses of randomized controlled trials (RCTs) have lower statistical power, and real-world evidence (RWE) is becoming widely recognized as a valuable source of evidence. The purpose of this study is to investigate methods for including RWE in a rare events meta-analysis of RCTs and the impact on the level of uncertainty around the estimates. Methods: Four methods for the inclusion of RWE in evidence synthesis were investigated by applying them to two previously published rare events meta-analyses: the naïve data synthesis (NDS), the design-adjusted synthesis (DAS), the use of RWE as prior information (RPI), and the three-level hierarchical models (THMs). We gauged the effect of the inclusion of RWE by varying the degree of confidence placed in RWE. Results: This study showed that the inclusion of RWE in a rare events meta-analysis of RCTs could increase the precision of the estimates, but this depended on the method of inclusion and the level of confidence placed in RWE. NDS cannot consider the bias of RWE, and its results may be misleading. DAS resulted in stable estimates for the two examples, regardless of whether we placed high- or low-level confidence in RWE. The results of the RPI approach were sensitive to the confidence level placed in RWE. The THM was effective in allowing for accommodating differences between study types, while it had a conservative result compared with other methods. Conclusion: The inclusion of RWE in a rare events meta-analysis of RCTs could increase the level of certainty of the estimates and enhance the decision-making process. DAS might be appropriate for inclusion of RWE in a rare event meta-analysis of RCTs, but further evaluation in different scenarios of empirical or simulation studies is still warranted. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) are powerful tools in graph application areas. However, recent studies indicate that GNNs are vulnerable to adversarial attacks, which can lead GNNs to easily make wrong predictions for downstream tasks. A number of works aim to solve this problem but what criteria we should follow to clean the perturbed graph is still a challenge. In this paper, we propose GSP-GNN, a general framework to defend against massive poisoning attacks that can perturb graphs. The vital principle of GSP-GNN is to explore the similarity property to mitigate negative effects on graphs. Specifically, this method prunes adversarial edges by the similarity of node feature and graph structure to eliminate adversarial perturbations. In order to stabilize and enhance GNNs training process, previous layer information is adopted in case a large number of edges are pruned in one layer. Extensive experiments on three real-world graphs demonstrate that GSP-GNN achieves significantly better performance compared with the representative baselines and has favorable generalization ability simultaneously. [ABSTRACT FROM AUTHOR]

Word embedding aims to represent each word with a dense vector which reveals the semantic similarity between words. Existing methods such as word2vec derive such representations by factorizing the word–context matrix into two parts, i.e., word vectors and context vectors. However, only one part is used to represent the word, which may damage the semantic similarity between words. To address this problem, this paper proposes a novel word embedding method based on point-wise mutual information criterion (PMIVec). Our method explicitly learns the context vector as the final word representation for each word, while discarding the word vector. To avoid the damage of semantic similarity between words, we normalize the word vector during the training process. Moreover, this paper uses point-wise mutual information to measure the semantic similarity between words, which is more consistent with human intuition on semantic similarity. Experiments on public data sets show that our PMIVec model can consistently outperform state-of-the-art models. [ABSTRACT FROM AUTHOR]

Efficacy of acupuncture-related therapy in the treatment of knee osteoarthritis: a network meta-analysis of randomized controlled trials. Knee osteoarthritis (KOA) is a degenerative disease in which articular cartilage and synovial fluid in the knee are lost, leading to lower limb disability, particularly in elderly individuals.[1] While nonsteroidal anti-inflammatory drugs (NSAIDs) can improve the physical function of KOA patients, they pose a significant risk of gastrointestinal adverse events that hinder disease management.[2] Acupuncture (AT) is one of the most common non-pharmacological therapies for the treatment of musculoskeletal disease-related symptoms.[3] Although evidence supports the potential effectiveness of acupuncture in treating KOA-related symptoms, the findings are controversial.[4] Therefore, we conducted a systematic review, quantitative analysis, and summary of existing evidence to investigate the safety and effectiveness of acupuncture for KOA. Acupuncture Trialists C. The persistence of the effects of acupuncture after a course of treatment: a meta-analysis of patients with chronic pain. [Extracted from the article]

Background: Ground-level ozone (O3) pollution is currently the one of the severe environmental problems in China. Although existing studies have quantified the O3-related health impact and economic loss, few have focused on the acute health effects of short-term exposure to O3 and have been limited to a single temporal and spatial dimension.Methods: Based on the O3 concentration obtained from ground monitoring networks in 334 Chinese cities in 2015-2018, this study used a two-stage exposure parameter weighted Log-linear exposure-response function to estimate the cause-specific mortality for short-term exposure to O3.Results: The value of statistical life (VSL) method that were used to calculate the economic loss at the city-level. Our results show that in China, the national all-cause mortality attributed to O3 was 0.27(95% CI: 0.14-0.55) to 0.39 (95% CI: 0.20-0.67) million across 2015-2018. The estimated economic loss caused by O3 was 387.76 (95% CI: 195.99-904.50) to 594.08 (95% CI: 303.34-1140.65) billion CNY, accounting for 0.52 to 0.69% of total reported GDP. Overall, the O3 attributed health and economic burden has begun to decline in China since 2017. However, highly polluted areas still face severe burden, and undeveloped areas suffer from high GDP losses.Conclusions: There are substantial health impacts and economic losses related to short-term O3 exposure in China. The government should pay attention to the emerging ozone pollution, and continue to strengthen the intervention in traditional priority areas while solving the pollution problem in non-priority areas. [ABSTRACT FROM AUTHOR]

• Estimates of PM 2.5 concentrations in 2030 declined compared with 2015. • If the age structure maintains, PM 2.5 -related premature death in 2030 related to 2015 decreased. • With the population aging, PM 2.5 -related premature death would sharply increase in 2030. Fine particulate matter (PM 2.5) pollution is one of the most critical environmental and public health problems in China and has caused an enormous disease burden, especially long-term PM 2.5 exposure. Global climate change represents another environmental challenge in the coming decades and is also an essential factor affecting PM 2.5 pollution. Moreover, China has an aging population with a changing population size and falling age-standardized mortality rates. However, little evidence exists evaluating the potential impacts from climate change and population aging on the long-term PM 2.5 exposure-related disease burden. This study quantifies the impacts of climate and population changes on changes in the disease burden attributed to long-term PM 2.5 exposure from 2015 to 2030 in mainland China, which could add evidence for the revision of relevant environmental standards and health policies. This modeling study investigated long-term PM 2.5 exposure-related mortality across China based on PM 2.5 projections under Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs) and population scenarios from shared socioeconomic pathways (SSPs). PM 2.5 concentrations were simulated by the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) modeling systems. In addition, three types of population projections in 2030 relative to 2015 were set up as follows: (i) the population remained the same as that in 2015; (ii) the population size changed under SSPs, but the age structure remained the same; (iii) both the population size and age structure changed under SSPs. The global exposure mortality model (GEMM) was adopted to estimate PM 2.5 -related premature deaths. Ambient PM 2.5 concentrations decreased from 2015 to 2030 under the two climate and emission scenarios. Estimates of related premature mortality in 2030 declined compared with that in 2015 due to lower PM 2.5 concentrations (RCP4.5: −16.8%; RCP8.5: −16.4%). If the age structure of the population remained unchanged and the population size changed under SSPs, the nonaccidental premature mortality also showed a decrease ranging from −18.6% to −14.9%. When both population size and age structure changed under SSPs, the population in China would become older. Nonaccidental premature mortality would sharply increase by 35.7–52.3% (with a net increase of 666–977 thousand) in 2030. The PM 2.5 pollution in 2030 under both RCP4.5 and RCP8.5 would slightly improve. The population sizes in 2030 projected by SSPs are relatively stable compared with that in 2015. However, the modest decrease due to air pollution improvement and stable population size would be offset by population aging. [ABSTRACT FROM AUTHOR]

Objective: Alteplase is the current standard of care for acute ischemic stroke. Tenecteplase is a newer fibrinolytic agent with preferable administration and lower costs; however, its comparative effectiveness to alteplase remains uncertain. We set out to perform a systematic review and meta-analysis to establish the benefits and harms of tenecteplase versus alteplase for acute ischemic stroke. Methods: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov from inception to April 2023 for randomized and non-randomized studies that compared tenecteplase versus alteplase for acute ischemic stroke. Paired reviewers independently assessed risk of bias and extracted data. We performed both conventional meta-analyses and Bayesian network meta-analyses (NMA) with random-effects models and used the GRADE approach to evaluate the certainty of evidence. Our primary efficacy outcome was excellent functional outcome at 3 months, defined as a score of 0–1 on the modified Rankin Scale. Our primary safety outcomes were symptomatic intracranial hemorrhage and all-cause mortality. Results: Thirty-six studies were eligible for review, including 12 randomized (n = 5533) and 24 non-randomized studies (n = 44,956). Moderate certainty evidence showed that there was no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months (odds ratio [OR], 1.10; 95% CI 0.98–1.23; risk difference [RD] 2.4%, 95% CI − 0.5 to 5.2), while moderate certainty evidence from NMA suggested that 0.25 mg/kg tenecteplase significantly improved excellent functional outcome at 3 months (OR, 1.16; 95% credible interval 1.02–1.32). Moderate certainty evidence showed that, compared to alteplase, tenecteplase may make little to no difference in the prevalence of symptomatic intracranial hemorrhage (OR, 1.12; 95% CI 0.79–1.59; RD 0.3%, 95% CI − 0.5 to 1.4), and probably reduces all-cause mortality (adjusted odds ratio [aOR], 0.44; 95% CI 0.30–0.64; RD − 4.6%; 95% CI − 5.8 to − 2.9). Conclusions: Moderate certainty evidence suggested that there was little to no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months and the risk of symptomatic intracranial hemorrhage, while compared to alteplase, tenecteplase probably reduce all-cause mortality. Administration of 0.25 mg/kg tenecteplase after acute ischemic stroke is suggestive of increasing the proportion of patients that achieve excellent functional outcome at 3 months. [ABSTRACT FROM AUTHOR]

Exposure to ambient fine particulate matter (PM 2.5) increases the mortality burden. Exposure windows and exposure-response functions (ERFs) are two critical components of accurate mortality burden estimation. We explored the potential heterogeneity of exposure windows and reassessed the PM 2.5 -attributable mortality burden in China with novel ERFs. Based on 1 km × 1 km satellite-retrieved PM 2.5 and population data, provincial-level age structure, and mortality data, we applied the recent Global Exposure Mortality Model (GEMM) with multiple exposure windows (1-year to 6-year during 2010–2015) to estimate age-specific PM 2.5 -attributable mortality burden in China in 2015. Then, the Global Burden of Disease (GBD) 2017 Integrated Exposure-Response (IER) and Log-Linear (LL) models were exercised for comparative analysis. The PM 2.5 -attributable mortality was the highest with a 3-year average exposure window (2013–2015). The GEMM-based total premature deaths were 133.2% [95% confidence interval (95% CI): 93.6%–226.4%] and 14.2% (95% CI: 13.9%–16.8%) higher than the values obtained from the GBD2017 IER model and LL model, respectively. The national mortality burden attributable to PM 2.5 was 1.94 (95% CI: 1.63–2.23) million, of which IHD and stroke were the leading causes, accounting for 27.3% and 23.0% of the total burden respectively. The mortality burden for the people over 80 years old was 0.62 (95%CI: 0.52–0.71) million, accounting for 31.9% (95%CI: 31.8%–32.0%) of the total burden. This study demonstrates the potential heterogeneity of PM 2.5 -attributable mortality burden associated with different exposure windows, especially when there are spatial-temporal variations in PM 2.5 concentrations. The model comparison results suggest that the health impacts attributed to long-term PM 2.5 exposure in China may be much higher than previously estimated. The population over 80 years old has the highest PM 2.5 -attributable mortality burden. These findings have important policy implications for addressing air pollution at the provincial and national level in China. • First explore the potential heterogeneity of exposure windows on mortality burden. • The PM 2.5 -related mortality burden in China was reassessed using the novel GEMM. • Mortality burden from PM 2.5 was estimated as 1.94 million in China in 2015. • GEMM-based excess deaths were 133.2% and 14.2% higher than that of IER and LL. • The population over 80 years old has the most serious mortality burden. [ABSTRACT FROM AUTHOR]

Existing studies focused on the evaluation of health burden of long-term exposure to air pollutants, whereas limited information is available on short-term exposure, particularly in China. Air pollutant concentrations in 338 Chinese cities in 2017 were used to estimate the air pollutants related health burden which was defined as premature mortalities from all-cause, cardiovascular and respiratory disease as well as hospital admissions for cardiovascular and respiratory disease. Log-linear model was used as the exposure-response function to estimate the health burden attributable to each air pollutant. The value of statistical life and cost of illness methods were used to estimate economic loss of the premature mortalities and hospital admissions, respectively. The national all-cause premature mortalities attributable to all air pollutants was 1.35 million, accounting for 17.2% of reported deaths in China in 2017. Among all-cause premature mortality, contributions of PM 2.5 , PM 2.5-10 , NO 2 , SO 2 , O 3 and CO were11.1%, 5.2%, 28.9%, 9.6%, 23.0%, and 22.2%, respectively. The national cardiovascular and respiratory premature mortalities were 0.77 and 0.21 million, respectively. About 7.8 million cardiovascular and respiratory disease hospital admissions were attributed to short-term exposure to all air pollutants. The economic loss of the overall health burden (premature mortality and hospital admissions) was 2065.54 billion Yuan, which was equivalent to 2.5% of the national GDP in 2017. The health burden and economic loss attributable to short-term exposure to ambient air pollutant are substantial in China. It suggested that the adverse health effects attributable to short-term exposure to air pollutant should not be neglected in China. In order to reduce the health impact of air pollution, each city should develop air pollution prevention and control measures based on existing scientific evidence. • The mortalities and hospital admissions attributable to short-term exposure to six air pollutants were estimated in China. • The national all-cause premature mortality attributable to all air pollutants was 1.35 million. • About 7.8 million hospital admissions were attributed to short-term exposure to air pollutants. • The economic loss of the overall health burden was 2065.54 billion Yuan, accounting for 2.5% of the national GDP. [ABSTRACT FROM AUTHOR]

Introduction: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. Objective: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. Methods: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. Results: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. Conclusion: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study. [ABSTRACT FROM AUTHOR]

Objective: Time‐varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time‐varying treatments. This study aimed to examine the current methods for dealing with time‐varying treatments in observational studies and developed practical recommendations. Methods: We searched PubMed from 2000 to 2021 for methodological articles about time‐varying treatments, and qualitatively summarized the current methods for handling time‐varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. Results: Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time‐varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time‐varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time‐independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention‐to‐treat, per‐protocol, or as‐treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. Conclusions: There is no consensus process for assessing treatment effects in observational studies with time‐varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice. [ABSTRACT FROM AUTHOR]

Background: The acupoint selections impact the effects of acupuncture, and preliminary evidence showed potential connection between pain threshold (PT) and acupuncture response. This study examined whether acupuncture at acupoints with lower PT versus higher PT would yield different effects in patients with knee osteoarthritis (KOA). Methods: In this multicenter randomized clinical trial, patients were randomly assigned (1:1:1) to receive acupuncture at acupoints with lower PT (LPT group), acupuncture at acupoints with higher PT (HPT group), and no acupuncture (waiting-list group). PT was measured with electronic von Frey detector. The primary outcome was the change in WOMAC total score from baseline to 16 weeks, and the secondary outcomes were SF-12 score, and active knee range of motion (ROM). Intention-to-treat analysis was conducted with linear mixed-effect model. Results: Among 666 randomized patients, 625 (93.84%) completed the study. From baseline to 16 weeks, patients in the LPT group versus HPT group had similar effects in reducing WOMAC total score (adjusted mean difference (MD) 2.21, 95% confidence interval (CI) −2.51 to 6.92, P = 0.36), while a greater reduction in WOMAC total score was observed in LPT group (−9.77, 95% CI −14.47 to −5.07, P < 0.001) and HPT group (−11.97, 95% CI −16.71 to −7.24, P < 0.001) compared with waiting-list group. There were no differences in SF-12 score and knee ROM between LPT versus HPT groups. Conclusion: Our findings found that the effects of acupuncture at acupoints with lower versus higher PT were similar, both were effective for patients with KOA. Trial registration: ClinicalTrials.gov identifier: NCT03299439. Registered 3 October 2017, https://clinicaltrials.gov/ct2/show/NCT03299439 [ABSTRACT FROM AUTHOR]