Awareness of and potential for dependent error in the observational epidemiologic literature: A review.

<bold>Purpose: </bold>Measurement error discussions often assume classification errors of key variables are independent. Yet, small amounts of dependent error can create large biases in effect estimates. The purpose of this review was to evaluate frequency of measurement error discussions and potential for dependent error in the observational literature.<bold>Methods: </bold>Two samples of articles analyzing exposure-outcome contrasts were collected: a random sample (n = 100) from high-impact epidemiology and medical journals (June 2015-July 2016), and a citation-based sample (n = 39) of studies citing one of two prominent dependent misclassification articles (through July 2016). We extracted study details, recorded measurement error mentions, and qualitatively assessed dependent error potential.<bold>Results: </bold>Measurement error was often discussed. No random sample articles explicitly mentioned dependent error, compared with 59% of the citation-based sample. The random sample was found to be at low risk of exposure-outcome (15% plausible/probable) but increased risk for exposure-confounder (38% plausible/probable) dependency. The citation-based sample was at higher risk for dependent error (exposure-outcome: 46% plausible/probable; exposure-confounder: 61% plausible/probable).<bold>Conclusions: </bold>Although measurement error was frequently mentioned, potential impact on observed results was rarely discussed in-depth or quantified. Dependent error mentions were rare, even among studies deemed susceptible. Further education and steps to avoid dependent error are needed. [ABSTRACT FROM AUTHOR]