A quantitative screening method to detect rater-introduced bias in clinical ratings

Background

We suggest a ‘screening test’ to examine large data files with clinical ratings for the occurrence of rater-introduced bias prior to using the data for quantitative analyses. The test is based on a statistical model in which a well-standardized interval-scale outcome (for example, milk yield) is related to clinical ratings (for example, body condition scores) obtained from multiple contexts (for example, dairy herds).

Findings

84,968 calvings from 279 herds, with subsequent body condition scores performed by 117 veterinarians within the first 21 days postpartum were analyzed with a multilevel random coefficient regression model. The model included an independent variable, where body condition score was centered within veterinarian. This is a so-called comparison effect to describe possible rater-introduced bias in the body condition scores. A highly significant comparison effect was found for second and older parities, indicating occurrence of possible rater-introduced bias in this large multi-herd data file.

Conclusions

A within-group centering technique (the comparison effect) appeared to be useful for discriminating between biased and unbiased clinical scores. In some cases, this test for bias should prevent further analysis of the data and divert the focus of study to the calibration of raters or alternative study designs.