Abstract
Most meta-analysis has concentrated on combining of treatment effect measures based on comparisons of two treatments. Meta-analysis of multi-arm trials is a key component of submission to summarize evidence from all possible studies. In this paper, an exact binomial model is proposed by using logistic regression model to compare different treatment in multi-arm trials. Two approaches such as unconditional maximum likelihood and conditional maximum likelihood have been determined and compared for the logistic regression model. The proposed models are performed using the data from 27 randomized clinical trials (RCTs) which determine the efficacy of antiplatelet therapy in reduction venous thrombosis and pulmonary embolism.
Highlights
The proposed models are performed using the data from 27 randomized clinical trials (RCTs) which determine the efficacy of antiplatelet therapy in reduction venous thrombosis and pulmonary embolism
There has been a massive growth in the number of randomized clinical trials (RCTs) since the first RCT was introduced in the well-known streptomycin trial in 1946 [1]
An important aspect of most reviews is the quantitative synthesis of results; meta-analysis is the statistical part of systematic review
Summary
There has been a massive growth in the number of randomized clinical trials (RCTs) since the first RCT was introduced in the well-known streptomycin trial in 1946 [1]. For multi-arm trials data, Chootrakool and Shi [3] introduced the normal approximation model using an empirical logistic transform requiring a large number of individual observations and the probability of being the case to be not too near zero or one. Most existing methods for meta-analysis of multi arm trials use the logistic regression model with the unconditional approach. Weber et al [21] used random effect model using the unconditional maximum likelihood method for the meta-analysis. They conducted a simulation study comparing two zero-cell corrections under the ordinary random-effects model. Treewai study for sparse network of trial including multi-arm trials They compared Bayesian and Frequentist methods in random effects network meta-analysis. The final section is discussion and conclusion including the advantages and the limitations of the two approaches
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