摘要
Objective To investigate the use of the Gibbs Sampler method in evaluating the relationship between clinic events and health risks in a meta analysis of multiple clinical trials Methods By using a generalized linear model with random effects, Gibbs Sampler technique was used in a meta analysis of multiple clinical trials of angiotensin converting enzyme (ACE) inhibitors in patients with myocardial infarction (MI) Results When heterogeneity across different trials can not be ignored, compared with the classic method, the odds ratio of relative reinfarction risk estimated by the Gibbs Sampler method would have less variation The gain in the reduction of variation in estimate of the overall odds ratio was 9 52% Conclusion Implementation of the Gibbs Sampler technique in meta analysis of multiple clinical trials has the potential of reducing the inaccuracy caused by heterogeneity across trials
Objective To investigate the use of the Gibbs Sampler method in evaluating the relationship between clinic events and health risks in a meta analysis of multiple clinical trials Methods By using a generalized linear model with random effects, Gibbs Sampler technique was used in a meta analysis of multiple clinical trials of angiotensin converting enzyme (ACE) inhibitors in patients with myocardial infarction (MI) Results When heterogeneity across different trials can not be ignored, compared with the classic method, the odds ratio of relative reinfarction risk estimated by the Gibbs Sampler method would have less variation The gain in the reduction of variation in estimate of the overall odds ratio was 9 52% Conclusion Implementation of the Gibbs Sampler technique in meta analysis of multiple clinical trials has the potential of reducing the inaccuracy caused by heterogeneity across trials
