Recruiting Control Participants into Stroke Biomarker Studies

Abstract

The number of scientists using -omics technologies to investigate biomarkers with the potential to gauge risk and aid in the diagnosis, treatment, and prognosis of stroke continues to rise, yet there are few resources to aid investigators in recruiting control participants. In this review, we describe two major strategies to match control participants to a stroke cohort-propensity score matching and one-to-one matching-including statistical approaches to gauge the balance between groups. We then explore the advantages and disadvantages of traditional recruitment methods including approaching spouses of enrolled stroke participants, direct recruitment from clinics, community outreach events, approaching retirement communities, and buying samples from a 3rd party vendor. Newer methods to identify controls by screening the electronic health record and using an online screening questionnaire are also described. Finally, we cover compensation for control participants and special considerations. The hope is that this review will serve as a roadmap whereby an investigator can successfully tailor their control recruitment strategy to the research question at hand and the local research environment. While this review is focused on blood-based biomarker studies, the principles will apply to investigators studying a broad range of biological materials.

Keywords: Biomarkers; Electronic health record; Matched groups; Propensity scores; Research subject recruitment; Stroke.

Figure 1.

Standardized difference (black, left y-axis) and P-values (gray, right y-axis) for a hypothetical matching variable with a 5% difference in prevalence between the control and stroke cohorts. A standardized difference < 0.1 or a P-value > 0.05 are generally reported as evidence for good matching between groups. The P-value, however, changes dramatically based on sample size such that a small study with 20 participants produces P-values much larger than a study with 200 participants. In contrast, for the same difference in prevalence between groups, the standardized difference remains the same whether there are 20 or 200 participants. Thus, the standardized difference is the preferred method to report match fidelity between variables. With either method, variables that are either over- or underrepresented in the population require more stringent matching.