Scientific Bias Arising from Sampling, Selective Recruitment, and Attrition: The Case for Improved Reporting

In human research, the ability to generalize study findings is incumbent not only on an accurate understanding of the study protocol and measures but also on a clear understanding of the study population. Differential recruitment and attrition has the potential of introducing bias and threatening the generalizability of study findings; yet, relatively few scientific publications report data on sampling, subject exclusion, and dropout. A 4-month census sampling (September-December 2009) of research articles and short communications in this journal (n = 116) was no exception. Among articles in which such data were appropriate to report, only 44% documented response rates, 53% described subjects who were excluded, and 10% performed analyses on enrollee versus nonenrollee differences; moreover, of the 17 longitudinal or intervention studies evaluated, only 3 of 17 reported dropout rates, and of those, only 2 of 3 reported reasons for dropout or an analysis that compared the characteristics of dropouts with those of completers. Given Cancer Epidemiology, Biomarkers and Prevention's mission to enhance the dissemination of unbiased scientific findings, we propose that guidelines regarding sample description, as defined by CONSORT, STROBE, or STREGA, be adopted by our journal for both observational and interventional studies that accurately describe the study population from the point of contact. Cancer Epidemiol Biomarkers Prev; 20(3); 415-8. (C)2011 AACR.