Adaptive designs were primarily used but inadequately reported in early phase drug trials.

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 ß = 0.14, p < 0.01) and the multicenter trials (estimated ß = 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.

Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.

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.

Comparing various Bayesian random-effects models for pooling randomized controlled trials with rare events.

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.

Spatiotemporal assessment of health burden and economic losses attributable to short-term exposure to ground-level ozone during 2015-2018 in China.

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.

Postoperative controlling nutritional status score is an independent risk factor of survival for patients with small hepatocellular carcinoma: a retrospective study.

BACKGROUND: The controlling nutritional status (CONUT) score has been widely used to evaluate the nutritional and immunological status. Clinical value of postoperative CONUT (PoCONUT) score in hepatocellular carcinoma (HCC) remains unknown. This study assessed whether PoCONUT score could serve as a useful predictor of survival for patients with small HCC. METHODS: 547 consecutive patients with small HCC who underwent liver resection between February 2007 and December 2015 were included in this retrospective case-control study. Patients were categorized into two groups: low PoCONUT group (PoCONUT score ≤ 2, n = 382) and high PoCONUT group (PoCONUT score ≥ 3, n = 165). Propensity score matching (PSM) analysis was applied to balance the bias in baseline characteristics. A cumulative survival curve was established by the Kaplan-Meier method, and differences in OS and RFS among CONUT score groups were determined by the log rank test. Cox proportional hazard regression analysis was used to evaluate the association of PoCONUT score and overall survival (OS) and recurrence-free survival (RFS), with calculation of hazard ratios (HRs) and 95 % confidence intervals (95 % CIs). RESULTS: Cox proportional hazard regression analysis suggested that the PoCONUT score was an independent risk factor for both OS and RFS in patients with small HCC before and after PSM. CONCLUSIONS: High PoCONUT score helps to predict worse OS and RFS in patients with small HCC who underwent liver resection.

Estimating health burden and economic loss attributable to short-term exposure to multiple air pollutants in China.

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 PM2.5, PM2.5-10, NO2, SO2, O3 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.

A Robust Framework for One-Shot Key Information Extraction via Deep Partial Graph Matching.

Text field labelling plays a key role in Key Information Extraction (KIE) from structured document images. However, existing methods ignore the field drift and outlier problems, which limit their performance and make them less robust. This paper casts the text field labelling problem into a partial graph matching problem and proposes an end-to-end trainable framework called Deep Partial Graph Matching (dPGM) for the one-shot KIE task. It represents each document as a graph and estimates the correspondence between text fields from different documents by maximizing the graph similarity of different documents. Our framework obtains a strict one-to-one correspondence by adopting a combinatorial solver module with an extra one-to-(at most)-one mapping constraint to do the exact graph matching, which leads to the robustness of the field drift problem and the outlier problem. Finally, a large one-shot KIE dataset named DKIE is collected and annotated to promote research of the KIE task. This dataset will be released to the research and industry communities. Extensive experiments on both the public and our new DKIE datasets show that our method can achieve state-of-the-art performance and is more robust than existing methods.

The Use of Real-World Evidence for Regulatory Decisions in China.

There is a growing demand for the use of high-quality real-world evidence (RWE) to support regulatory decision-making worldwide and in China, which highlights the need for conducting literature reviews to evaluate the available data and evidence. This study aims to review the use of RWE in Chinese regulatory decisions and to summarize relevant regulatory and methodological considerations to inform the future use of RWE in China. We identified policy documents, technical guidance documents, and cases on official Chinese government websites and extracted their contents separately. We consulted experts from the National Medical Products Administration (NMPA) and academic institutes and searched case-related articles for enrichment. We also searched and included articles related to the use of RWE/Real-world data in Chinese regulatory decisions. Six trial versions of technical guidance documents, 7 case studies, and 40 articles related to the Chinese regulatory decisions were included in this study. Based on the technical guidance, data quality, and appropriate study design and statistical analysis are the main concerns for RWE generation. The cases and articles related to regulatory decisions revealed 9 main concerns, including data sources and applicability, data quality, strength of existing evidence, appropriate study design and statistical analysis, regulated and transparent process for analysis and evidence generation, product safety and efficacy, product characteristics and clinical needs, ethical considerations and data security, and communicate adequately with regulatory authorities. Among these concerns, data issues are central. Preliminary attempts have been made by the NMPA to promote the use of RWE, but substantial challenges still remain.

Handling time-varying treatments in observational studies: A scoping review and recommendations.

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.

A systematic survey of adaptive trials shows substantial improvement in methods is needed.

OBJECTIVES: To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. STUDY DESIGN AND SETTING: We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. RESULTS: We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. CONCLUSION: Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.

Sample size calculations for randomized controlled trials with repeatedly measured continuous variables as primary outcomes need improvements: a cross-sectional study.

OBJECTIVES: Randomized controlled trials (RCTs) with repeatedly measured continuous variables as primary outcomes are common. Although statistical methodologies for calculating sample sizes in such trials have been extensively investigated, their practical application remains unclear. This study aims to provide an overview of sample size calculation methods for different research questions (e.g., key time point treatment effect, treatment effect change over time) and evaluate the adequacy of current practices in trial design. STUDY DESIGN AND SETTING: We conducted a comprehensive search of PubMed to identify RCTs published in core journals in 2019 that utilized repeatedly measured continuous variables as their primary outcomes. Data were extracted using a predefined questionnaire including general study characteristics, primary outcomes, detailed sample size calculation methods, and methods for analyzing the primary outcome. We re-estimated the sample size for trials that provided all relevant parameters. RESULTS: A total of 168 RCTs were included, with a median of four repeated measurements (interquartile range 3-6) per outcome. In 48 (28.6%) trials, the primary outcome used for sample size calculation differed from the one used in defining the primary outcomes. There were 90 (53.6%) trials exhibited inconsistencies between the hypotheses specified for sample size calculation and those specified for primary analysis. The statistical methods used for sample size calculation in 158 (94.0%) trials did not align with those used for primary analysis. Additionally, only 6 (3.6%) trials accounted for the number of repeated measurements, and 7 (4.2%) trials considered the correlation among these measurements when calculating the sample size. Furthermore, of the 128 (76.2%) trials that considered loss to follow-up, 33 (25.8%) used an incorrect formula (i.e., N∗(1+lose rate) for sample size adjustment. In 53 (49.5%) out of 107 trials, the re-estimated sample size was larger than the reported sample size. CONCLUSION: The practice of sample size calculation for RCTs with repeatedly measured continuous variables as primary outcomes displayed significant deficiencies, with a notable proportion of trials failed to report essential parameters about repeated measurement required for sample size calculation. Our findings highlight the urgent need to use optimal sample size methods that align with the research hypothesis, primary analysis method, and the form of the primary outcome.

Tenecteplase versus alteplase for acute ischemic stroke: a systematic review and meta-analysis of randomized and non-randomized studies.

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.

Methods for the Inclusion of Real-World Evidence in a Rare Events Meta-Analysis of Randomized Controlled Trials.

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.

Projected burden of stroke in China through 2050.

BACKGROUND: Stroke is the leading cause of death in China, and predicting the stroke burden could provide essential information guiding the setting of medium- and long-term health policies and priorities. The study aimed to project trends associated with stroke burden in China through 2050, not only in terms of incidence and mortality but also for prevalence and disability-adjusted life years (DALYs). METHODS: Data on stroke rates in incidence, prevalence, deaths, and DALYs in China between 1990 and 2019 were obtained from a recent Global Burden of Disease study. Demographic-specific trends in rates over time were estimated using three models: the loglinear model, the Lee-Carter model, and a functional time series model. The mean absolute percentage error and the root mean squared error were used for model selection. Projections up to 2050 were estimated using the best fitting model. United Nations population data were used to project the absolute numbers through 2050. RESULTS: From 2019 to 2050, the crude rates for all measures of the stroke burden are projected to increase continuously among both men and women. We project that compared with those in 2019, the incidence, prevalence, deaths, and DALYs because of stroke in China in 2050 will increase by 55.58%, 119.16%, 72.15%, and 20.04%, respectively; the corresponding increases in number were 2.19, 34.27, 1.58, and 9.21 million. The age-standardized rate is projected to substantially decline for incidence (8.94%), death (40.37%), and DALYs (43.47%), but the age-standardized prevalence rate is predicted to increase by 10.82%. By 2050, the burden of stroke among the population aged ≥65 years will increase significantly: by 104.70% for incidence, by 218.48% for prevalence, by 100.00% for death, and by 58.93% for DALYs. CONCLUSIONS: With the aging population in China increasing over the next three decades, the burden of stroke will be markedly increased. Continuous efforts are needed to improve stroke health care and secondary prevention, especially for older adults.

Comparison of statistical methods for integrating real-world evidence in a rare events meta-analysis of randomized controlled trials.

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.

Evaluating the impact of including non-randomised studies of interventions in meta-analysis of randomised controlled trials: a protocol for a meta-epidemiological study.

INTRODUCTION: Although interest in including non-randomised studies of interventions (NRSIs) in meta-analysis of randomised controlled trials (RCTs) is growing, estimates of effectiveness obtained from NRSIs are vulnerable to greater bias than RCTs. The objectives of this study are to: (1) explore how NRSIs can be integrated into a meta-analysis of RCTs; (2) assess concordance of the evidence from non-randomised and randomised trials and explore factors associated with agreement; and (3) investigate the impact on estimates of pooled bodies of evidence when NRSIs are included. METHODS AND ANALYSIS: We will conduct a systematic survey of 210 systematic reviews that include both RCTs and NRSIs, published from 2017 to 2022. We will randomly select reviews, stratified in a 1:1 ratio by Core vs non-Core clinical journals, as defined by the National Library of Medicine. Teams of paired reviewers will independently determine eligibility and abstract data using standardised, pilot-tested forms. The concordance of the evidence will be assessed by exploring agreement in the relative effect reported by NRSIs and RCT addressing the same clinical question, defined as similarity of the population, intervention/exposure, control and outcomes. We will conduct univariable and multivariable logistic regression analyses to examine the association of prespecified study characteristics with agreement in the estimates between NRSIs and RCTs. We will calculate the ratio of the relative effect estimate from NRSIs over that from RCTs, along with the corresponding 95% CI. We will use a bias-corrected meta-analysis model to investigate the influence on pooled estimates when NRSIs are included in the evidence synthesis. ETHICS AND DISSEMINATION: Ethics approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis, and interpretation of meta-analysis that integrate RCTs and NRSIs.

Effectiveness and Safety of Two Consecutive Cycles of Single Embryo Transfer Compared With One Cycle of Double Embryo Transfer: A Systematic Review and Meta-Analysis.

Objective: To date, evidence regarding the effectiveness and safety of two consecutive cycles of single embryo transfer (2SETs) compared with one cycle of double embryo transfer (DET) has been inadequate, particularly considering infertile women with different prognostic factors. This study aimed to comprehensively summarize the evidence by comparing 2SETs with DET. Methods: PubMed, Embase, Cochrane Library databases, ClinicalTrails.gov, and the WHO International Clinical Trials Registry Platform were searched up to March 22, 2022. Peer-reviewed, English-language randomized controlled trials (RCTs) and observational studies (OS) comparing the outcomes of 2SETs with DET in infertile women with their own oocytes and embryos were included. Two authors independently conducted study selection, data extraction, and bias assessment. The Mantel-Haenszel random-effects model was used for pooling RCTs, and a Bayesian design-adjusted model was conducted to synthesize the results from both RCTs and OS. Main Results: Twelve studies were finally included. Compared with the DET, 2SETs were associated with a similar cumulative live birth rate (LBR; 48.24% vs. 48.91%; OR, 0.97; 95% credible interval (CrI), 0.89-1.13, τ2 = 0.1796; four RCTs and six observational studies; 197,968 women) and a notable lower cumulative multiple birth rate (MBR; 0.87% vs. 17.72%; OR, 0.05; 95% CrI, 0.02-0.10, τ2 = 0.1036; four RCTs and five observational studies; 197,804 women). Subgroup analyses revealed a significant increase in cumulative LBR (OR, 1.33; 95% CrI, 1.29-1.38, τ2 = 0) after two consecutive cycles of single blastocyst transfer compared with one cycle of double blastocyst transfer. Moreover, a lower risk of cesarean section, antepartum hemorrhage, preterm birth, low birth weight, and neonatal intensive care unit admission but a higher gestational age at birth and birth weight were found in the 2SETs group. Conclusion: Compared to the DET strategy, 2SETs result in a similar LBR while simultaneously reducing the MBR and improving maternal and neonatal adverse outcomes. The 2SETs strategy appears to be especially beneficial for women aged ≤35 years and for blastocyst transfers.

Use of statistical methods among acupuncture randomized controlled trials was far from satisfactory.

OBJECTIVES: To examine whether appropriate statistical methods were used in acupuncture randomized controlled trials (RCTs). STUDY DESIGN AND SETTING: We searched PubMed to identify acupuncture RCTs with continuous outcome as primary outcome published in the core clinical journals and complementary and alternative medicine (CAM) journals between January 2010 and December 2019 (10 years). We compared statistical characteristics of included trials published in core clinical journals and CAM journals. RESULTS: We included 262 RCTs, including 46 published in core clinical journals and 216 in CAM journals. Of included RCTs, only 132 (50.4%) clearly predefined the primary outcome, 72 (27.5%) specified the use of intention to treat or modified intention to treat population for primary analysis. In the 167 trials reported missing participant data (MPD), 118 (70.7%) used suboptimal methods (e.g., complete case analysis) for dealing with MPD; 11 (6.6%) conducted sensitivity analysis regarding MPD. Among the 161 trials with repeated measures design, only 21 (13.0%) used advanced statistical models (e.g., mixed-effects models) for handling repeated-measure data in the primary analysis. In the 72 trials involving multiple acupuncturists, only 4 (5.6%) adjusted acupuncturist variable or considered the clustering by acupuncturist in analysis. Trials in core clinical journals were more likely to predefine primary outcome (78.3% vs. 44.4%, P < 0.001), use multiple imputations for handling MPD (40% vs. 1.5%, P < 0.001), and use statistically advanced methods for assessing treatment effect at a single time point (26.1% vs. 2%, P = 0.001). CONCLUSION: The use of statistical methods among acupuncture RCTs is far from satisfactory. Our findings highlighted the need for researchers to carefully use the optimal statistical methods and for journal editors to strengthen the use of statistical methods.

Missing data were poorly reported and handled in randomized controlled trials with repeatedly measured continuous outcomes: a cross-sectional survey.

BACKGROUND AND OBJECTIVES: Missing data are common in randomized controlled trials (RCTs) involving repeatedly measured continuous outcomes. Evidence on the reporting and handling of such outcome data is lacking, which has prevented further improvement in methods and reporting of RCTs. METHODS: We searched PubMed to identify RCTs published in the Core Clinical Journals in 2019 that reported a continuous primary outcome with repeated measures. A team of investigators conducted a study screening and collected data using pilot-tested, standardized questionnaires from a random sample of eligible RCTs. We thoroughly collected information about the reporting of missing data for the repeatedly measured continuous outcome and the methods used to handle the missing data. RESULTS: We included 200 eligible trials, whose mean number of repeated measures for the continuous primary outcomes was 5.46 (SD = 3.4). Sixty-one (30.5%) trials explicitly reported missing data at both participant and outcome levels, 116 (58.0%) at the participant level only, and 2 (1.0%) at the outcome level only. Sixty (30.0%) trials reported missing data at the participant level by group and by time point, and 53 (26.5%) at the outcome level by group and by time point. Among 179 trials having reported missing data, 162 (90.5%) did not assess the balance of baseline characteristics, 143 (79.9%) did not assume missing mechanism; 65 (36.3%) used suboptimal methods for handling missing data (e.g., complete case analysis); 41 (22.9%) conducted sensitivity analyses, and 5 (11.9%) assumed alternative missing mechanisms for sensitivity analyses. CONCLUSION: The reporting of missing data for repeatedly measured continuous outcomes were inadequate and the use of statistical methods for handling missing data was far from optimal. Substantial efforts are warranted to improve the reporting and statistical handling of these outcome data.