Controlled clinical trials
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Control Clin Trials · Dec 1997
ReviewClinical trials with multiple outcomes: a statistical perspective on their design, analysis, and interpretation.
This article tackles both practical and statistical issues in the handling of multiple outcomes in clinical trials, with relevance to trial design, analysis, and reporting. Specific topics illustrated by examples include: the advantage of prespecifying priorities amongst outcomes and analyses, corrections for multiple significance testing and their limited value, problems with adverse event data, the use of a single global test of significance for clinically related outcomes, the use of a combined outcome for clinical event data, and the value of exploring interrelationships amongst outcomes. The problems in handling multiple outcomes are enhanced by trials being too small, dichotomous attitudes (is the trial "positive" or not?), obsession with p-values, and the manipulative instincts of human nature. While predeclarations of priorities in analysis and reporting of multiple outcomes are important in suppressing distortive claims, it would be unfortunate if too inflexible an approach suppressed unpredictable findings from being seriously considered.
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Control Clin Trials · Dec 1997
Review Comparative StudyMeta-analysis of randomized trials: looking back and looking ahead.
Meta-analyses as currently practiced are usually retrospective. They can be made more rigorous by developing a protocol that incorporates prospectively the elements that are usually necessary in a well-designed trial. ⋯ Once the large trials have been completed, they could be brought together within the framework of a meta-analysis to estimate the overall treatment effect with greater confidence and to explore the effects in various subgroups. This article explores the value and limitations of meta-analyses and suggests ways of improving their conduct and interpretation.
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Control Clin Trials · Dec 1997
Comparative StudyCumulating evidence from randomized trials: utilizing sequential monitoring boundaries for cumulative meta-analysis.
We propose the adaptation of classical monitoring boundaries for use in cumulative meta-analysis as guidelines for deciding when accumulating evidence is statistically significant and medically convincing. The interpretation of information from a randomized controlled trial is compared with that from a meta-analysis. The concept of optimal information size for a meta-analysis is developed and used to adapt monitoring boundaries to cumulative meta-analysis.