Anita van Zwieten, Fiona M Blyth, Germaine Wong and Saman Khalatbari-Soltani
Epidemiologists are typically nicely outfitted to design and conduct research that minimise varied varieties of bias, in order to acquire essentially the most correct estimates doable and subsequently high-quality proof. In observational research, some varieties of bias, like confounding, have acquired a whole lot of consideration, whereas others have been neglected. One which has been uncared for is overadjustment bias, which happens when researchers modify for an explanatory variable on the causal pathway from publicity to final result when in search of to estimate the full impact.
Confounding happens when a 3rd variable that causes each the publicity and the end result biases the estimated affiliation. It’s generally handled by adjusting for potential confounders within the statistical fashions. Overadjustment bias typically occurs as a result of researchers understand adjustment as universally innocent or useful as a way to take care of confounding. In actuality, relying on the variables adjusted for and the underlying causal mannequin, adjustment might be useful, don’t have any impression, and even – as within the case of overadjustment – have detrimental impacts on the accuracy of estimates.
As an example, overadjustment is prone to end in bias in the direction of the null, resulting in an underestimation of the full impact. As an instance this, researchers highlighted the impression that overadjustment would have on their whole impact of curiosity (academic inequalities in well being amongst individuals with persistent kidney illness) by constructing varied fashions with totally different ranges of adjustment and explicitly evaluating the outcomes. They confirmed that the relative danger of vascular occasions for individuals with no formal training, in contrast with these with a tertiary training, was lowered from 1.46 of their most well-liked mannequin (confounder-adjusted solely) to 1.15 in a mannequin that additionally included mediators, together with well being behaviours, illness development, and comorbidities.
There are additionally circumstances the place overadjustment could result in bias in any path, similar to when the adjusted variable is a collider – a variable that’s brought on by two or extra variables by way of two or extra distinctive causal paths.
Overadjustment is a typical downside in lots of fields of epidemiology. As we’ve got beforehand mentioned in a primer, it’s particularly related in social epidemiology due to the complicated, upstream and multifaceted pathways between social exposures and well being outcomes. For instance, overadjustment could happen if a researcher adjusts for health-related behaviours when making an attempt to estimate the full impact of training on mortality (Determine 1). This can be a downside as a result of it’s prone to result in an underestimation of the impact of training on mortality.
Enterprise a scientific evaluation of observational research is a posh job that requires researchers to mitigate many potential sources of bias within the included research, to make sure that their conclusions are strong sufficient to tell coverage and apply selections. Given the potential impression of overadjustment bias on research findings, we questioned how systematic reviewers navigate this.
In our scoping evaluation printed in IJE, we developed 12 standards based mostly on earlier literature on overadjustment bias and used these to have a look at potential approaches to managing overadjustment bias in 84 systematic evaluations of well being inequalities. Total, these approaches weren’t usually utilized. As an example, <5% of evaluations clearly outlined confounders and mediators, constructed causal diagrams, or thought-about overadjustment of their risk-of-bias evaluation. In distinction, 54% included confounding of their risk-of-bias evaluation.
Our findings are regarding, given the impression that underestimation of well being inequalities may have on social and well being insurance policies, which in flip have an effect on the lives of many individuals. We made sensible suggestions that researchers from varied disciplines can use to handle overadjustment and guarantee it doesn’t compromise evaluation findings (Determine 2).
We questioned whether or not the restricted consideration of overadjustment that we noticed in systematic evaluations may be as a consequence of a lack of information of this subject within the analysis group. So, we then investigated what related steering reviewers have entry to when conducting systematic evaluations and meta-analyses of observational research.
In our opinion piece additionally printed in IJE, we reviewed 12 key risk-of-bias or crucial appraisal instruments (e.g. High quality in Prognosis Research device, ROBINS-I, ROBINS-E) and 10 key tips (e.g. Cochrane Handbook for Systematic Evaluations of Interventions, Conducting Systematic Evaluations and Meta-Analyses of Observational Research of Etiology [COSMOS-E] and JBI Handbook for Proof Synthesis) for systematic evaluations and meta-analyses of observational research, to contemplate the extent to which they thought-about overadjustment bias and confounding bias. Solely three newer risk-of-bias instruments (ROBINS-I, ROBINS-E and the Confounder Matrix) explicitly thought-about overadjustment. In distinction, all 12 of the instruments explicitly thought-about confounding. Not one of the 10 tips gave specific steering on overadjustment bias, whereas 4 did for confounding bias.
We suggest that overadjustment bias be given specific consideration in new revisions of tips for systematic evaluations and meta-analyses. We additionally encourage evaluation authors to undertake the newer risk-of-bias instruments, which embrace consideration of overadjustment.
Extra broadly, there’s a want to lift consciousness of the significance of balancing overadjustment and confounding biases when conducting main research and evaluations. This requires considered consideration of which variables are applicable to regulate for in a given context. Typically there isn’t any easy reply, however speaking transparently about our assumptions permits strong dialogue and fosters high-quality proof. These points should be highlighted not solely in evaluation tips and instruments but in addition in epidemiological coaching, journal peer evaluation, and publication processes to make sure that epidemiologists generate strong estimates that can be utilized successfully to enhance the well being of communities and sort out well being inequalities.
Learn extra:
van Zwieten A, Dai J, Blyth FM, Wong G, Khalatbari-Soltani S. Overadjustment bias in systematic evaluations and meta-analyses of socio-economic inequalities in well being: a meta-research scoping evaluation. Int J Epidemiol 2024; 53: dyad177
van Zwieten A, Blyth FM, Wong G, Khalatbari-Soltani S. Consideration of overadjustment bias in tips and instruments for systematic evaluations and meta-analyses of observational research is lengthy overdue. Int J Epidemiol 2024; 53: dyad174
Dr Anita van Zwieten (@anitavanzwieten) is a lecturer and social epidemiologist on the College of Sydney College of Public Well being and the Centre for Kidney Analysis at Westmead. She has analysis experience in life-course approaches to socioeconomic inequalities in well being, well being inequalities, and socioeconomic outcomes amongst individuals with persistent kidney illness, and methodological points in social epidemiology.
Professor Fiona Blyth AM (@fionablyth2) is a professor of public well being and ache medication on the College of Sydney and an ARC Centre of Excellence in Inhabitants Ageing Analysis (CEPAR) Chief Investigator. She is a public well being doctor and ache epidemiologist who has been concerned in research of persistent ache epidemiology for nearly 20 years, together with giant potential cohort research, randomised managed trials, pharmacoepidemiological research, and well being companies analysis utilizing linked, routinely collected datasets.
Professor Germaine Wong (@germjacq) is the Director of Western Renal Service at Westmead Hospital, a professor of medical epidemiology, NHMRC Management Fellow on the College of Sydney and Co-Director of Medical Analysis on the Centre for Kidney Analysis. She has an internationally recognised observe file in transplant epidemiology, most cancers and transplantation, social ethics in organ allocation, determination analytical modelling, well being economics, and quality-of-life research in transplant recipients.
Dr Saman Khalatbari-Soltani (@saamaankh) is a social epidemiologist and senior lecturer in inhabitants well being on the College of Sydney College of Public Well being and CEPAR. Her analysis encompasses social determinants of well being, wholesome ageing, well being inequalities, and the function of behavioural, psychological and organic components within the genesis of well being inequalities at older ages throughout the life course.
