Abstract
Objectives It is well known that a sequentially monitored clinical trial that stops early for benefit has a crude treatment difference that overestimates the true treatment effect. This has led to extended debate in the literature, with some researchers arguing that early stopping is an important source of bias in meta-analyses of clinical trials. We therefore investigated the implications of excluding studies that stopped early, so-called truncated studies, from estimation of treatment effects.
Highlights
As well as estimation bias, we studied information bias measured as the difference between standard measures of the statistical information, such as sample size, and the actual information based on the conditional sampling distribution
We found exclusion of truncated studies leads to both estimation bias and information bias
The magnitude of information bias is an increasing function of the magnitude of estimation bias. This has important implications for metaanalyses that typically weight by sample size. It means that studies with the most biased treatment effect are the most overweighted studies in a meta-analysis
Summary
It is well known that a sequentially monitored clinical trial that stops early for benefit has a crude treatment difference that overestimates the true treatment effect. This has led to extended debate in the literature, with some researchers arguing that early stopping is an important source of bias in meta-analyses of clinical trials. We investigated the implications of excluding studies that stopped early, so-called truncated studies, from estimation of treatment effects
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
