Identifying Falsified Clinical Data

Clinical data serve as a necessary basis for medical decisions. Consequently, the importance of methods that help officials quickly identify human tampering of data cannot be underestimated. In this paper, we suggest Benford’s Law as a basis for objectively identifying the presence of experimenter distortions in the outcome of clinical research data. We test this tool on a clinical data set that contains falsified data and discuss the implications of using this and information-theoretic methods as a basis for identifying data manipulation and fraud.

Clinical data serve as a necessary basis for choices relative to medical decisions that reduce health risks. The changing health recommendations that fill our newspapers and television screens reminds us how fragile these data are. In addition to the usual noise in experimental data, researchers have been known to massage the data to achieve a particular result or to reach a certain statistical significance level.