What is volunteer bias

Entries Per Page:. Methods Map Research Methods. Explore the Methods Map. Related Content. Back to Top. Find content related to this author. Retention and participation were associated with socio-demographic variables, smoking status and variables reflecting leverage, saliency and altruism. Trial findings were modified by data weighting to account for volunteer bias Table 5. From single site research , we cannot assume that respondents and response patterns are representative of other populations.

Trial location may have influenced recruitment, retention, and sample donation. For example, attitudes towards blood donation differ between communities [53] — [55]. This trial was restricted to healthy dyads. However, cohort studies report similar clinic attendance rates [56]. The balance between benefit and harm is more uncertain in prevention or vaccine trials involving healthy participants than in therapeutic trials [57].

Further work is needed to explore generalisation of these findings to trials involving unwell or hospitalised children, where recruitment is restricted to closely defined populations with current medical conditions [14] , [58] — [60].

Comparison with external data was the only option available to evaluate demographic representation at recruitment; however, some ages, locations and time-frames were not entirely congruent.

Therefore, we tested this approach by comparing the deprivation scores and rankings of respondents with those of women giving birth in the same timeframe and geographical areas. We are unaware of other trials testing sample selection using this approach. The similarity between the comparisons indicates that it would be reasonable to assess volunteer bias using Census data where closely matched population data is unavailable. The data sources used for comparison are themselves vulnerable to social desirability, volunteer and non-response bias, and may not be fully representative of the population.

Accordingly, our calculations may underestimate demographic imbalance. Reports of behaviour are vulnerable to social desirability response biases , but we have no reason to assume that our data would be uniquely vulnerable.

Non-contact bias should be distinguished from volunteer bias [13]. Marginalised women may not access care or only accept domiciliary care in refuges, so would neither have received our invitation letters nor been approached Table 1.

Interpretation of weighted analyses rests with readers; this strategy to account for volunteer or non-response bias is routine in observation studies, including UK birth cohorts [25] , [63] — [65]. We acknowledge the limitations of post hoc subgroup analyses [66] , [67] , and present these solely to illustrate how outcome distribution affects data weighting, not to guide clinical practice.

Low numbers in outcome variables necessitate cautious interpretation; however, these findings merit exploration in pooled data sets and meta-analysis. Recruitment strategies in this trial favoured wealthier families with healthier behaviours, as in observation studies [17] — [19] , [23] — [25] , cluster [26] and adult prevention trials [10] , [12] , [27].

Significant degrees of sub-optimal recruitment and potential volunteer bias are relatively recent phenomena [68] , [69]. Just as recruitment to trials is becoming increasingly difficult [2] , successive UK birth cohorts have had lower response rates. Retention was influenced by socio-demographic and less tangible factors.

The most disadvantaged and smokers were less likely to participate in follow-up, attend clinics, consent to skin-prick testing or blood sample donation. Treatment allocation had no negative impact. Multivariate analyses indicated that when demographics were accounted, leverage, saliency [13] , [74] and altruism [14] , [75] are important predictors of participation Table 4.

To our knowledge, this has not been tested in trial data. The saliency and leverage of the trial, clinic or skin-prick testing, and the theory of social exchange [74] , [76] , [77] featured in binary, threshold decisions to participate. Opportunities to see consultant paediatricians and receive allergen testing may have been particularly attractive to carers of infants experiencing adverse events or rashes.

Access to treatment [22] or expectation of better attention incentivise participation [59] , [78]. Altruism was important in the decision to consent to venous blood sample donation by well infants. Requests for time and biological samples deter many potential trial participants [1] , [17] , [30] , [80] , [81]. However, participants consented to sample donation.

Such altruism is more evident in less recent trials [75]. Applying the concept of volunteer bias to trial data tests the generalisability, external validity, transferability, utility and dependability of trial findings. Keyword searches in three databases PubMed, Web of Science, Scopus indicate that data weighting to account for and quantify potential volunteer bias is rarely undertaken in paediatric prevention trials. Although an unrepresentative sample does not necessarily mean that findings would not be replicated in a wider population, research quality criteria include non-biased sample selection [82].

Strategies to account for missing data, such as sensitivity analysis, do not address volunteer bias [84]. It cannot be assumed that participation and attrition are random events, prompting calls for full details of target or eligible populations to be reported for all trials [20].

Problems were confined to the most materially disadvantaged and smokers. Non-targeted recruitment and retention risk volunteer bias and disenfranchisement of the least affluent and most marginalised, where childhood ill-health is concentrated [85].

However, asthma was less common in the over-represented group. For this outcome, it will be important to consider any potential loss of power, as the event rate proportion may differ between the population and the recruited and retained samples.

Weighting increased the leverage of data from the most deprived participants Table 5. Accordingly, this confirmed the robustness of positive outcomes concentrated in under-represented groups atopic sensitisation. Weighting techniques, standard practice in cohort studies [25] , [63] — [65] , based on demographic distribution at recruitment, can augment analyses of trial data [8] , [41]. Such weighting is based on assumptions that participants from disadvantaged groups are representative or typical of their groups in all respects, including attributes not recorded; only careful fieldwork and local knowledge can support such suppositions.

Obviating any need for such subjective judgments, and obtaining trial evidence on which to base practice recommendations to the wider, target population, necessitates engagement, recruitment and retention of fully representative samples [34] , [82]. Strategies include:. Additional resources. However, the disadvantaged are disproportionately hard to reach [13] , [21].

To safeguard investment in clinical trials, the research community should budget sufficient time and resources for complicated, personalised contact and follow up procedures [18] , [28] , as in birth cohorts [25] , [63]. Stratification of the population and over-sampling those least likely to participate, as in cohort studies [25] , [63] — [65]. More work is needed to evaluate this approach and assess the traceability of respondents. Weighted analysis to account for residual problems.

Accounting for all possible confounders will be difficult, but even partial mapping strengthens the analysis [41]. If trial evidence is to reflect population diversity, demographically representative samples should be recruited and retained. Disproportionate socio-demographic representation arising at recruitment intensified throughout the trial.

Accounting for this by data weighting to assess volunteer bias modified important trial findings. Whether this would occur in other trials warrants investigation. However, material deprivation is not the only predictor of participation. The leverage-saliency theory of research participation remains important; additionally, these findings indicate that altruism should not be discounted. Application of the concept of volunteer bias to clinical trials suggests that to offer reassurance regarding the generalisability, external validity, transferability, utility and dependability of findings, researchers should quantify differences between recruited samples and target populations and weight data to protect findings from potential distortion by volunteer bias.

Variables entered into regression models, whole sample and sample retained at 6 months. Occupational groups in recruited sample and Census for South West Wales: mothers. Occupational groups in recruited sample and Census for South West Wales: fathers.

This study uses anonymised data held in the Secure Anonymised Information Linkage SAIL system, which is part of the national e-health records research infrastructure for Wales. We should like to acknowledge all the data providers who make anonymised data available for research. Cultech Ltd. UK part funded the trial and provided the probiotic and matching placebo, and generated the random allocation sequence.

Sue Plummer is a Director of Cultech and advised on study design and contributed to the final report. The sponsors were not involved in data collection, analysis or interpretation of the findings. The Knowledge Exploitation Fund had no involvement in the conduct of the trial. The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.

Sue Plummer proposed the product to be researched and commented on the study design. Sue Plummer and Dr. Iveta Garaiova commented on the manuscript prepared by Sue Jordan to ensure clarity. No changes to the findings were either suggested or made.

National Center for Biotechnology Information , U. PLoS One. Published online Jul 9. Allen , 2 Caroline J. Brooks , 2 Iveta Garaiova , 3 Martin L. Heaven , 2 Ruth Jones , 2 Sue F.

Plummer , 3 Ian T. Russell , 2 Catherine A. Thornton , 2 and Gareth Morgan 2. Steven J. Caroline J. Martin L. Sue F. Ian T. Catherine A. Robert K. Hills, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: S. Jordan, S. Allen, C. Thornton, A. Watkins, R. Jones, I. Russell, C. Brooks, M. Heaven, and G. Morgan declare no conflicts of interest. Storey received financial support from Cultech Ltd. Garaiova is a Senior Research Manager and S.

The product in the trial was not commercially available at the time of the trial. Received Dec 21; Accepted May This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. This article has been cited by other articles in PMC. Associated Data Supplementary Materials Table S1: Variables entered into regression models, whole sample and sample retained at 6 months.

Abstract Background The vulnerability of clinical trials to volunteer bias is under-reported. Methods and Results This paper extends the applications of the concept of volunteer bias by using data from a trial of probiotic supplementation for childhood atopy in healthy dyads to explore 1 differences between a trial participants and aggregated data from publicly available databases b participants and non-participants as the trial progressed 2 impact on trial findings of weighting data according to deprivation Townsend fifths in the sample and target populations.

Conclusions Potential for volunteer bias intensified during the trial, due to non-participation of the most deprived and smokers. The potential consequences of volunteer bias might be summarised [28] : Volunteer bias threatens the generalisability or external validity, transferability, and utility of findings and detracts from their clinical value [20].

This paper aims to extend the application of the concept of volunteer bias to clinical trials, using data from a paediatric trial, by exploring: Potential for Volunteer Bias Differences between the recruited sample and the target population. The Trial As reported elsewhere [35] , [36] , this randomised, double-blind, placebo-controlled, parallel-group trial assessed the effects of probiotic food supplements on key immune parameters and prevention of atopy and atopic conditions asthma, eczema and allergic rhinitis in young children.

Recruitment Strategy A multifaceted recruitment strategy was designed to contact the whole population of pregnant women in the catchment area Table 1. Table 1 Recruitment strategies considered. Not labour intensive. Risks non-contact bias by failing to contact those not booking, typically the most disadvantaged. Assumes literacy. Relies on health service staff. Recruitment [13] , [90] , and retention [91] demanded a labour-intensive face to face approach. Advertising costs. Impact may be disappointing [75] , and difficult to quantify.

We observed little impact. Following TV coverage, we received five telephone calls, all from women living outside the catchment area or already delivered. Two 0. Monetary incentives No The most effective strategy to improve recruitment. There was general recognition that research could only happen and medical management could only improve if families were willing to join trials.

Targeted Personal approach in hospital antenatal clinics. Ben Davis July 20, How might volunteer bias affect the results of a survey? Why would volunteers in a study have a bias towards it? What is the volunteer bias? What is wrong with volunteer sampling? How does RCT reduce bias? How do you control allocation bias? Can you adjust for selection bias? Why is selection bias a problem? What is the importance of overcoming biases? Which one is a technique of overcoming the barriers of decision making?

What are the three major barriers to effective decision making? What are the problems managers face in decision making? What are three things that can prevent effective decision making?