ObjectivesTo assess the accuracy of different types and magnetic field intensity of cardiac magnetic resonance for coronary artery disease.MethodsPubMed, The Cochrane Library, EMbase, WanFang Data, CNKI and CBM databases were searched to collect the studies on different types and magnetic field intensity of cardiac magnetic resonance for coronary artery disease from inception to May 15th, 2017. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, data were synthesized by using MetaDisc 1.4, RevMan 5.3 and Stata 12.0 softwares. The pooled sensitivity (Sen), pooled specificity (Spe), pooled positive likelihood ratio (+LR), pooled negative likelihood ratio (–LR), pooled diagnostic odds ratio (DOR) and the area under curve (AUC) of the summary receiver-operating characteristic curve (SROC) were used to assess the diagnostic value of different types and magnetic field intensity of cardiac magnetic resonance.ResultsTwenty diagnostic studies were included, which involved 1 357 patients. The results of meta-analysis showed that (1) based on patient: compared with the gold standard, the pooled Sen, Spe, +LR, –LR, DOR and the AUC of SROC, pre-test probability, post-test probability were (0.87, 95%CI 0.82 to 0.90), (0.88, 95%CI 0.82 to 0.92), (7.33, 95%CI 4.74 to 11.32), (0.15, 95%CI 0.11 to 0.20), (49.53, 95%CI 27.46 to 89.36), (0.93, 95%CI 0.91 to 0.95), 20.00% and 65.00%, respectively. (2) Based on blood vessels: the pooled Sen, Spe, +LR, –LR, DOR and the AUC of SROC, pre-test probability, post-test probability were (0.81, 95%CI 0.76 to 0.85), (0.87, 95%CI 0.81 to 0.91), (6.37, 95%CI 4.37 to 9.30), (0.22, 95%CI 0.17 to 0.27), (29.58, 95%CI 18.53 to 47.22), (0.89, 95%CI 0.86 to 0.92), 20.00% and 61.00%, respectively. (3) Subgroup analysis showed that there was no difference in AUROC of different types of cardiac magnetic resonance, but significant difference was found in AUROC of 1.5T and 3.0T magnetic field intensity.ConclusionsCurrent evidence shows that, compared with gold standard, cardiac magnetic resonance can be regarded as an effective and feasible method for preoperative staging of breast cancer.
Indirect comparison refers to a comparison of different healthcare interventions using data from separate studies, and is often used because of a lack of, or insufficient evidence from head-to-head comparative trials. We aimed to summarize the definition, fundamental theory, type, relevant statistical contents, and to clarify some question on how to use indirect comparison, in order to attract more researchers' attention and promote methodological development of indirect comparison.
Objective To systematically review the effectiveness and safety of Tanreqing for curing the hand-foot-mouth disease. Methods Such databases as PubMed, EMbase, CENTRAL, CBM, CNKI, VIP and WanFang Data are electronically searched to collect the randomized controlled trials (RCTs) on the effectiveness and safety of Tanreqing for hand-foot-mouth disease till February 2013. According to the inclusion and exclusion criteria, literature was screened, data were extracted, and the methodological quality of included studies was also assessed. Then, meta-analysis was performed using RevMan 5.2.7 software. Results Twelve RCTs on Tanreqing versus ribavirin involving 1 258 cases and 27 RCTs on Tanreqing plus ribavirin versus ribavirin involving 3 289 cases were included. The results of meta-analysis showed that, compared to ribavirin, Tanreqing has higher total efficiency in the treatment of hand-foot-mouth disease (OR=5.03, 95%CI 3.28 to 7.71, Plt;0.000 01), cooling time (MD= –1.09, 95%CI –1.51 to –0.68, Plt;0.000 01), simplex regression time (MD= –0.90, 95%CI –1.20 to –0.60, Plt;0.000 01), and healing time (MD= –1.76, 95%CI –2.52 to –0.99, Plt;0.000 01), with significant differences. Compared to ribavirin, the group of Tanreqing plus ribavirin has higher total efficiency on treatment of hand-foot-mouth disease (OR=5.32, 95%CI 4.02 to 7.06, Plt;0.000 01), cooling time (MD= –1.32, 95%CI –1.63 to –1.01, Plt;0.000 01), simplex regression time (MD= –0.5, 95%CI –0.98 to –0.2, Plt;0.000 01), and healing time (MD= –1.41, 95%CI –1.83 to –0.98, Plt;0.000 01), with significant differences. The results of indirect comparative analysis showed that, there was no significant difference in the treatment options of Tanreqing plus ribavirin and Tanreqing alone concerning total efficiency, cooling time, simplex regression time, and healing time. Conclusion The study shows that Tanreqing alone and Tanreqing plus ribavirin are similar for curing the hand-foot-mouth disease, and both groups have better clinical effectiveness than ribavirin alone.
Randomized controlled trials (RCTs) are currently the gold standard for the treatment effect comparisons; however, it is sometimes not feasible to conduct an RCT due to ethical and economic reasons. In the absence of evidence for head-to-head RCT direct comparison, the indirect comparison technique is an effective and resource-saving alternative. Matching-adjusted indirect comparison (MAIC) is an attractive method in the field of population-adjusted indirect comparisons between two trials. It can adjust for between-trial imbalances in the distribution of observed covariates by weighting the available individual patient data of the studied intervention and then match the aggregated data of the controlled intervention. Subsequently, the treatment effect comparison can be evaluated through the post-matched population. Although MAIC is gaining increasing attention in clinical research, especially in the evaluation of new drugs, efforts are still largely required for knowledge dissemination in China. In this paper, we briefly introduced the concepts, research value and examples, and pros and cons of MAIC.
With the continuous progress of national medical insurance strategic purchasing and value-based healthcare, pharmacoeconomic evaluation, serving as a technical tool for assessing the cost-effectiveness of healthcare interventions, has played an important role in policy decision support. Comparative efficacy evidence is the core data source for pharmacoeconomic evaluation, and also the foundation for conducting pharmacoeconomic research. In recent years, the number of innovative drugs approved based on single-arm trial has been increasing. Most existing randomized controlled clinical trials are also placebo-controlled or compared with traditional treatments, unable to directly meet the need for efficacy evidence of comparisons with conventional or standard treatments in pharmacoeconomic evaluations. In the absence of direct comparative efficacy evidence, exploring indirect comparison methods for efficacy has become a cutting-edge direction in pharmacoeconomic evaluation. Through a comprehensive literature review and systematic analysis, this study focuses on five indirect comparison methods based on individual patient data for population adjustment, including match adjusted indirect comparison (MAIC), simulated treatment comparison (STC), propensity score matching (PSM), inverse probability of treatment weighting (IPTW) and network meta regression (NMR), and discussing their basic concepts, advantages and disadvantages and application comparisons. Finally, it provides methodological suggestions on how to choose an indirect comparison method for efficacy, with the aim of promoting the generation of higher-quality indirect comparison evidence for efficacy and advancing pharmacoeconomic evaluation to provide high-quality evidence references for healthcare policy decision-making.
ITC (Indirect Treatment Comparison) software and indirect procedure of Stata software are especially used for indirect comparison nowadays, both of which possess the characteristics of friendly concise interface and support for menu operation. ITC software needs the application of other software to yield effect estimation and its confidence interval of direct comparison firstly; while Stata-indirect procedure can complete direct comparison internally and also operate using commands, which simplifies complicated process of indirect comparison. However, both of them only perform "single-pathway" of data transferring and pooling, which is a common deficiency. From the results, their results are of high-degree similarity.
With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies, the application of unadjusted indirect comparisons and network meta-analysis methods has been limited. Matching-adjusted indirect comparison (MAIC) is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies, and complete the comparison of the efficacy between target interventions. This study introduced the concept and principles of MAIC. In addition, we demonstrated how to use the anchored MAIC method based on R language for survival data, which has been widely used in anti-cancer drug evaluation. This study aimed to provide an alternative method to inform evidence-based decisions.
Objective To provide methodological guidance for the application of matching-adjusted indirect comparison (MAIC). Methods The methodology literature on MAIC was examined to clarify key steps and methodological points, and MAIC application literature in the non-small cell lung cancer field published after January 2016 was systematically reviewed to compare and analyze the current status and problems of MAIC. Results MAIC consisted of five key steps: data source and sample selection, matching variable screening, individual weight calculation, matching validity evaluation, and relative efficacy calculation. The systematic review revealed that studies primarily employed literature reviews to screen data sources, used statistical analysis and other scientific methods to screen matching variables, employed software for individual weight calculation, evaluated matching validity by reporting effective sample size (ESS), calculated relative efficacy using Cox, logistic, and other models, conducted sensitivity analyses to evaluate the uncertainty caused by different data sources and matching variable combinations, and the studies demonstrated good overall reporting standardization but significant differences in particular aspects. Concerning the connection between MAIC and pharmacoeconomic research, studies included mainly used target drugs as the reference group of survival data extrapolation, and proportional hazards (PH) assumptions were considered when utilizing hazard ratios (HR) in extrapolation. Conclusion There are some deficiencies in the method application and reporting standards of MAIC research, such as lack of explanation of data source selection criteria and matching variable screening criteria, insufficient reporting of weight distribution, and inadequate consideration of PH assumptions. It is recommended that future MAIC research systematically screen data sources and report covariate distribution evaluation, covariate status evaluation, weight distribution, uncertainty measurement, etc. Additionally, considering PH assumptions after calculating HR is suggested.
Due to the lack of head to head direct comparison evidence, applying indirect comparison (ITC) as well as network meta-analysis to compare multiple interventions becomes a new popular and powerful statistical technique. However, its theoretical system still needs improvement. In this article, we briefly introduce and summarize its progress concerning basic concepts, method assumptions, influencing factors of effectiveness, and software for analysis, so as to help researchers better understand the method and promote its application in evidence production.
It is a challenge for clinicians and diagnostic systematic reviewers to determine the best test in clinical diagnosis and screening. Meanwhile, it also becomes the new chance and challenge for diagnostic test meta-analysis. Network meta-analysis has been commonly used in intervention systematic reviews, which can compare the effect size of all available interventions and to choose the best intervention. Network meta-analysis of diagnostic test can be defined as comparing all available diagnostic technologies in the same conditions based on the common reference tests. In order to provide the guide for diagnostic systematic reviewers, we aims to introduce four methods of conducting diagnostic test accuracy network meta-analysis, and to explore two ranking methods of network meta-analysis of diagnostic test accuracy.