Etiological and prognostic studies always directly reported effect size with its 95% confidence interval, hence, data transformation was needed when performing meta-analysis based on these studies. Using the data of risk ratio, hazard ratio, odds ratio and 95% confidence interval as an example, this paper introduces the process of using RevMan 5.3 software to convert data and perform meta-analysis.
Health economics analysis has become increasingly important in recent years. It is essential to master the use of relevant software to conduct research in health economics. TreeAge Pro software is widely used in the healthcare decision analysis. It can carry out decision analysis, cost-effectiveness analysis, and Monte Carlo simulation. With powerful functionlity and outstanding visualization, it can build Markov disease transition models to analyze Markov processes according to disease models and accomplish decision analysis with decision trees and influence diagrams. This paper introduces cost-effectiveness analysis based on Markov model with examples and explains the main graphs.
ObjectivesTo systematically review the efficacy and safety of nalmefene hydrochloride for acute cerebral infarction.MethodsPubMed, EMbase, The Cochrane Library, CBM, CNKI, WanFang Data and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on nalmefene hydrochloride for acute cerebral infarction from inception to February 21st, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 8 RCTs involving 1 038 patients were included. The results of meta-analyses showed that, compared to the routine treatment group, the nalmefene hydrochloride group was significantly associated with an increased reduction in total effective rate (RR=1.14, 95%CI 1.04 to 1.23, P=0.003), GCS (MD=1.30, 95%CI 0.66 to 1.94, P<0.0001), patient satisfaction (RR=1.26, 95%CI 1.03 to 1.55, P=0.03), cerebral blood flow (MD=5.00, 95%CI 3.81 to 6.19, P<0.05), and cerebral blood volume (MD=0.28, 95%CI 0.23 to 0.32, P<0.05). It was also significantly associated with an reduction of NIHSS, CSS, level of inflammatory factors after treatment in 14 days, level of MMP-9 and mean transit time of contrast medium (P<0.05). However, no significant association was observed between two groups in level of inflammatory factors after treatment in 20 days. For safety outcomes, no significant association was found between two groups in mortality, dizziness, and nausea and vomiting.ConclusionsThe current evidence indicates that the nalmefene hydrochloride can be used to treat acute cerebral infarction based on routine treatment of acute cerebral infarction, and the safety is relatively good. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusion.
Trial sequential analysis (TSA) can identify inclusive results of apparently conclusive of meta-analyses by providing require information size and monitoring boundary. Certain methods of calculating information size are existed. Our objective was to give a brief introduction of four methods to help readers to better perform TSA in making meta-analyses.
The sample size of a meta-analysis should not be less than a single randomized controlled trial. Trial sequential analysis (TSA) can provide required information size and monitoring boundary to justify the conclusion of meta-analysis. However, the TSA software is only suitable for binary and continuous data, and it cannot analyze the time-to-event data. This paper aimed to introduce how to analyze the time-to-event data using TSA approach.
The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.
Objective To detect the false-positive results of cumulative meta-analyses of Cochrane Urology Group with the trial sequential analysis (TSA). Methods The systematic reviews of Urology Group of The Cochrane Library were searched to collect meta-analyses with positive results. Two researchers independently screened literature and extracted data of included meta-analyses. Then, TSA was performed using TSA software version 0.9 beta. Results A total of 11 meta-analyses were included. The results of TSA showed that, 8 of 11 (72.7%) meta-analyses were potentially false-positive results for failing to surpass the trial sequential monitoring boundary and to reach the required information size. Conclusion TSA can help researchers to identify the false-positive results of meta-analyses.
Objective To detect the false-negative results of cumulative meta-analyses of Cochrane Urology Group with the trial sequential analysis (TSA). Methods The Urology Group of The Cochrane Library (Issue 6, 2016) was searched to collect meta-analyses with negative results. Two researchers independently screened literature and extracted data of included meta-analyses. Then, TSA was performed using TSA software version 0.9 beta. Results A total of 11 papers involving 12 meta-analyses were included. The results of TSA showed that, four (33%) out of 12 meta-analyses were potentially false-negative results for failing to surpass the trial sequential monitoring boundary and to reach the required information size. Conclusion Some of the negative results of systematic reviews from Cochrane Urology Group was false-negative. TSA can help researchers to identify the false-negative results of meta-analyses.
The association between single nucleotide polymorphism and disease is a typical representation of genetic association studies. Compared with the traditional dichotomous data, single nucleotide polymorphism data has its own characteristics, and 5 genetic models are commonly performed in meta-analysis. In this paper, we show how to use the " meta” package in R software to conduct meta-analysis of single nucleotide polymorphism research through examples.
To perform a meta-analysis of single nucleotide polymorphism needs to calculate gene frequency. This paper employs allele model as an example to introduce how to calculate gene frequency and display the process of a meta-analysis of single nucleotide polymorphism data using Review Manager 5.3 software.