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author:("Wei, minghui")
1.  Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis 
Statistics in Medicine  2014;34(6):984-998.
Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated.
The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta-analyses.
The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-study heterogeneity.
We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PMCID: PMC4383649  PMID: 25475839
meta-analysis; Bayesian methods; heterogeneity; prior distributions
2.  Comparative efficacy and safety of treatments for localised prostate cancer: an application of network meta-analysis 
BMJ Open  2014;4(5):e004285.
There is ongoing uncertainty about the optimal management of patients with localised prostate cancer.
To evaluate the comparative efficacy and safety of different treatments for patients with localised prostate cancer.
Systematic review with Bayesian network meta-analysis to estimate comparative ORs, and a score (0–100%) that, for a given outcome, reflects average rank order of superiority of each treatment compared against all others, using the Surface Under the Cumulative RAnking curve (SUCRA) statistic.
Data sources
Electronic searches of MEDLINE without language restriction.
Study selection
Randomised trials comparing the efficacy and safety of different primary treatments (48 papers from 21 randomised trials included 7350 men).
Data extraction
2 reviewers independently extracted data and assessed risk of bias.
Comparative efficacy and safety evidence was available for prostatectomy, external beam radiotherapy (different types and regimens), observational management and cryotherapy, but not high-intensity focused ultrasound. There was no evidence of superiority for any of the compared treatments in respect of all-cause mortality after 5 years. Cryotherapy was associated with less gastrointestinal and genitourinary toxicity than radiotherapy (SUCRA: 99% and 77% for gastrointestinal and genitourinary toxicity, respectively).
The limited available evidence suggests that different treatments may be optimal for different efficacy and safety outcomes. These findings highlight the importance of informed patient choice and shared decision-making about treatment modality and acceptable trade-offs between different outcomes. More trial evidence is required to reduce uncertainty. Network meta-analysis may be useful to optimise the power of evidence synthesis studies once data from new randomised controlled studies in this field are published in the future.
PMCID: PMC4024605  PMID: 24833678
Prostate Cancer; Treatment; Randomised Trials; Systematic Review; Meta-Analysis
4.  Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis 
BMC Research Notes  2012;5:476.
Cell-free fetal DNA (cffDNA) can be detected in maternal blood during pregnancy, opening the possibility of early non-invasive prenatal diagnosis for a variety of genetic conditions. Since 1997, many studies have examined the accuracy of prenatal fetal sex determination using cffDNA, particularly for pregnancies at risk of an X-linked condition. Here we report a review and meta-analysis of the published literature to evaluate the use of cffDNA for prenatal determination (diagnosis) of fetal sex. We applied a sensitive search of multiple bibliographic databases including PubMed (MEDLINE), EMBASE, the Cochrane library and Web of Science.
Ninety studies, incorporating 9,965 pregnancies and 10,587 fetal sex results met our inclusion criteria. Overall mean sensitivity was 96.6% (95% credible interval 95.2% to 97.7%) and mean specificity was 98.9% (95% CI = 98.1% to 99.4%). These results vary very little with trimester or week of testing, indicating that the performance of the test is reliably high.
Based on this review and meta-analysis we conclude that fetal sex can be determined with a high level of accuracy by analyzing cffDNA. Using cffDNA in prenatal diagnosis to replace or complement existing invasive methods can remove or reduce the risk of miscarriage. Future work should concentrate on the economic and ethical considerations of implementing an early non-invasive test for fetal sex.
PMCID: PMC3444439  PMID: 22937795
Cell-free fetal DNA; Meta-analysis; Non-invasive prenatal diagnosis
5.  Estimating within-study covariances in multivariate meta-analysis with multiple outcomes 
Statistics in Medicine  2012;32(7):1191-1205.
Multivariate meta-analysis allows the joint synthesis of effect estimates based on multiple outcomes from multiple studies, accounting for the potential correlations among them. However, standard methods for multivariate meta-analysis for multiple outcomes are restricted to problems where the within-study correlation is known or where individual participant data are available. This paper proposes an approach to approximating the within-study covariances based on information about likely correlations between underlying outcomes. We developed methods for both continuous and dichotomous data and for combinations of the two types. An application to a meta-analysis of treatments for stroke illustrates the use of the approximated covariance in multivariate meta-analysis with correlated outcomes. Copyright © 2012 John Wiley & Sons, Ltd.
PMCID: PMC3618374  PMID: 23208849
multivariate meta-analysis; correlated outcomes; nested events; delta method; within-study correlation

Results 1-5 (5)