Table 6.

Examples of types of bias during different phases of study progression, descriptions of bias, examples of their effect on outcomes and preventive measures to be taken to minimize bias

Phase	Type of bias	Description	Effect on outcome	Preventive measures
Planning	Selection bias	Errors during identification of study population or in any process of gathering sample	May undermine external validity and can lead to overestimation of e.g. accuracy	Choose prospective study design and enrol consecutively or randomly Perform a sample size calculation aligned with intended use of imaging method
	Spectrum bias	Included sample does not represent intended spectrum of disease severity Subgroup differences e.g. screening population vs specialist population	May influence disease prevalence and thereby study outcomes e.g. accuracy may be overestimated	Ensure that included participants are representative of those that imaging method is intended for
	Referral bias	A type of spectrum bias Referral pattern distorts sample distribution
Implementation	Information bias	Selective revealing or suppression of information when results are interpreted with knowledge of reference standard results with less or more information than in practice when imaging methods or treatment outcomes are compared and images are interpreted with knowledge of the intervention	May lead to status quo where multiple researchers discover and discard same outcomes May lead to overestimation of test performance	Provide detailed descriptions of information given prior to interpretation of index test, if possible ensure that intended use of test and/or normal clinical practices are followedStrive for blinding or double-blinding of subjects, researchers, technicians, data collector/analysts and evaluators or any persons involved, who may influence outcomes subjectively
	Performance bias	Conduct and/or interpretation of index test is inadequately performed and/or insufficiently reported	May affect study outcomes and will not enable comparison or meta-analyses of studies of same imaging method	Provide detailed descriptions of methods and consider appropriate number of raters and think carefully about raters’ expertise
	Classification bias	Reference standard does not correctly classify target condition	May affect disease prevalence and thereby study outcomes	Ensure reference standard correctly reflects patients within target condition
	Verification bias	A set of participants does not undergo reference standard	Usually leads to overestimation of sensitivity	Ensure all participants undergo both index test and reference standard
	Incorporation bias	Reference standard is not independent of index test	May lead to overestimation of accuracy	Ensure reference standard and index test are independent
Analysis Writing up	Verification bias	Attrition rate or reasons for withdrawals are not documented	Estimates may be inconclusive	Account for all participants who entered study Explain uninterruptable or intermediate test results
	Citation bias	Citation or non-citation of research findings, depending on nature and direction	May threaten validity of future research as some results may incorrectly receive more and more emphasis	Cite and discuss supportive as well as unsupportive high-quality studies Analyse original data
	Publication bias Multiple (duplicate) publication bias	Publication or non-publication of research findings, depending on nature and direction of results Multiple publications overlap each other substantially or same publication is published several times	May lead to incorrect estimation of intervention effects May distort results of systematic reviews and meta-analyses	Strategy depends on whether aim is to tackle sets of missing studies, or whether selective/incomplete reporting of data is a primary problem In systematic reviews construct a funnel plot where estimates of efficacy are plotted against e.g. sample size Update systematic reviews regularly