A scoping review published in BMC Medical Research Methodology has mapped the software landscape for two complementary approaches to sample selection bias: risk-of-bias tools, which help reviewers identify potential bias qualitatively, and quantitative bias analysis (QBA), which tests how sensitive a study’s conclusions are to plausible assumptions about the selection mechanism. The review, led by Audinga-Dea Hazewinkel with Elinor Curnow, Kate Tilling and Rachael A. Hughes, covered software published from January 2004 through August 2025 and highlights a striking imbalance between the two approaches.
The authors frame the problem as one with real consequences: failure to appropriately account for selection bias may lead to erroneous conclusions. Risk-of-bias tools are recommended in systematic reviews of health-related studies to flag potential selection bias, while QBA is recommended to assess quantitatively whether a meta-analysis or an individual study’s findings would hold under different assumptions about how samples were selected. Yet, as the review notes, the effective use of risk-of-bias tools is complicated by the sheer number of available instruments, their varying granularity and a high reliance on investigator expertise, while QBAs are not routinely implemented because of limited knowledge about accessible methods and software.
To be included, tools had to be validated risk-of-bias instruments or software that could be applied without code changes, still available in 2025, and accompanied by documentation. Against those criteria, the review identified 24 risk-of-bias tools. All of them contained between one and five signalling questions relating to selection bias, and together they covered a wide range of study designs, including randomised controlled trials, non-randomised studies and diagnostic accuracy studies.
The granularity of those questions varied considerably, and fewer than half of the tools included a dedicated selection bias domain. Only three of the 24 provided structured guidance for synthesising the evidence from their signalling questions into a final risk judgment on selection bias. The authors conclude that, while many tools are available, the degree of guidance they offer varies drastically, and the overall assessment of selection bias depends heavily on how the individual investigator interprets the questions. They suggest that tutorial-style articles with worked exemplars would help applied researchers, particularly those who are inexperienced and face a large volume of tools with wide variation in guidance.
On the quantitative side, the review identified nine software programs: one web-based tool, five R packages and three Stata commands. Their scope proved narrow. Six of the nine programs were applicable only when estimating the effect of a binary exposure on a binary outcome, a restriction that excludes many study questions. Among the approaches implemented, three programs offered probabilistic QBA, one offered multidimensional QBA and one offered tipping-point QBA. The remaining programs required users to supply their own code to carry out a full QBA.
The authors argue that greater provision of QBA software specifically for selection bias, together with detailed guidelines, would facilitate wider uptake among future studies. Their recommendations for the software community include publishing articles about new programs or posting them to well-known repositories so applied researchers can find them, and building in specialist features that support the entire QBA process, including the summarising of results, to encourage adoption.
The review’s key messages underline the practical asymmetry the authors found. For risk-of-bias assessment, the challenge is one of choice and interpretation: many validated tools exist, but researchers must navigate differing granularity and inconsistent guidance. For quantitative bias analysis, the challenge is one of availability: functioning, documented software exists, but most of it handles only a narrow slice of the study designs and effect measures that applied researchers encounter.
As a scoping review, the study maps what software exists rather than evaluating how well any individual tool performs in practice, and its inclusion criteria excluded tools requiring adaptation before use or lacking documentation. Even so, it offers the clearest available snapshot of a methodological gap: qualitative tools for spotting selection bias are plentiful but unevenly supported, while the quantitative methods that could test how much bias matters remain scarce, restricted and, in several cases, dependent on users writing their own code.
Subject of Research: Software implementations of risk-of-bias tools and quantitative bias analysis methods for sample selection bias
Article Title: Scoping review of software implementations of risk-of-bias tools and quantitative bias analysis methods for sample selection bias
Article References: Hazewinkel, A.-D., Curnow, E., Tilling, K., & Hughes, R. A. (2026). Scoping review of software implementations of risk-of-bias tools and quantitative bias analysis methods for sample selection bias. BMC Medical Research Methodology. https://doi.org/10.1186/s12874-026-03060-3
Image Credits: AI Generated
DOI: 10.1186/s12874-026-03060-3
Keywords: selection bias, risk-of-bias tools, quantitative bias analysis, scoping review, systematic reviews, R packages, Stata, research methods, epidemiology, software, Scoping, review
News Source: Ophelia Keating. (October 29, 2026). Review Maps Software for Assessing Selection Bias in Health Studies. Scienmag.
