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Author Ponnuraja, C. ♦ Venkatesan, P.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Anti Tuberculosis Chemotherapy ♦ Comprehensive Approach ♦ Bayesian Meta-analysis ♦ Randomized Controlled Trial ♦ Treatment Effect ♦ Tuberculosis Clinical Trial ♦ Specified Medical Condition ♦ Relevant Covariates ♦ Mcmc Algorithm ♦ Several Study ♦ Random Effect ♦ Bayesian Random Effect Model ♦ Frequentist Random Effect Model ♦ Reporting Result ♦ Relevant Trial ♦ Several Independent Trial ♦ Overall Treatment Efficacy ♦ Markov Chain Monte Carlo ♦ Simple Non Iterative Procedure ♦ Specific Therapeutic Recommendation ♦ Certain Treatment ♦ Different Aspect ♦ Bayesian Approach ♦ Consistent Assessment ♦ Reliable Estimate
Abstract Meta-analysis enables researchers to combine the results of several studies to get a reliable estimate. This paper examines the reviews and findings of sixteen randomized tuberculosis clinical trials and each reporting results from several independent trials. Each finding pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These studies require consistent assessment of homogeneity of treatment effect before pooling. This paper outlined some innovations in Meta-analysis using Markov chain Monte Carlo (MCMC) techniques for implementing Bayesian random effects models. Additionally we compared the Bayesian approach with frequentist random effects model. We discuss more in a random effects approach to combining the evidence from a series of experiments particularly comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple non iterative procedure for characterizing the distribution of treatment effects in a series of studies. These techniques allow different aspects of variation to be incorporated into descriptions of the association between studies. This work attempts to discuss the application of MCMC algorithm for high dimensional clinical trial tuberculosis data.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article