Bias busters: using the right risk-of-bias tools

作者：Madelon van Wely, Julie M. Hastings, Basil C. Tarlatzis, Rui Wang · 发表于：Human Reproduction Update · 年份：2025 · DOI：10.1093/humupd/dmaf016 · 被引用次数：2 · 研究领域：Statistical Methods in Clinical Trials、Meta-analysis and systematic reviews、Health Systems, Economic Evaluations, Quality of Life

Because systematic reviews support clinical decision-making, they are a crucial part of evidence-based practice. The actual value of a systematic review is determined by the quality of the studies that are included. To verify this quality, we need to look, amongst other things, at the risk of bias (RoB). Bias is the potential for a study’s results to be inaccurate because of problems with the study’s design, implementation, or reporting. It is possible that the recruitment procedures resulted in more severe cases being placed in the intervention arm, that each intervention group received different treatments (beyond the exposure being compared), that the outcomes were not consistently measured, or that there was not enough reporting. Because these problems can make an exposure or intervention appear better or worse than it actually is, we must look into them. Readers of the systematic review could be misinformed if bias is not taken into consideration. Different kinds of biases can arise in different study designs. For example, blinding and allocation concealment are two issues that randomized controlled trials (RCTs) may encounter. Confounding and selection bias are more likely to occur in observational studies. These differences require the use of specific RoB instruments tailored to the type of study. Over the past few decades, numerous RoB tools have been developed. According to a recent study, out of 226 RoB tools that were published between 1995 and 2023, 25% rated the ...