OBJECTIVE: To diagnose egocentric neglect after stroke, the spatial bias of performance on cancellation tasks is typically compared to a single cutoff. This standard procedure relies on the assumption that the measurement error of cancellation performance does not depend on non-spatial impairments affecting the total number of cancelled targets. Here we assessed the impact of this assumption on false-positive diagnoses. METHOD: We estimated false positives by simulating cancellation data using a binomial model. Performance was summarised by the difference in left and right cancelled targets (R-L) and the Centre of Cancellation (CoC). Diagnosis was based on a fixed cutoff versus cutoffs adjusted for the total number of cancelled targets and on single test performance versus unanimous or proportional agreement across multiple tests. Finally, we compared the simulation findings to empirical cancellation data acquired from 651 stroke patients. RESULTS: Using a fixed cutoff, the rate of false positives depended on the total number of cancelled targets and ranged from 10% to 30% for R-L scores and from 10% to 90% for CoC scores. The rate of false positives increased even further when diagnosis was based on proportional agreement across multiple tests. Adjusted cutoffs and unanimous agreement across multiple tests were effective at controlling false positives. For empirical data, fixed versus adjusted cutoffs differ in estimation of neglect prevalence by 13%, and this difference was largest for patients with non-spatial impairments. CONCLUSIONS: Our findings demonstrate the importance of considering non-spatial impairments when diagnosing neglect based on cancellation performance.
OBJECTIVE: To diagnose egocentric neglect after stroke, the spatial bias of performance on cancellation tasks is typically compared to a single cutoff. This standard procedure relies on the assumption that the measurement error of cancellation performance does not depend on non-spatial impairments affecting the total number of cancelled targets. Here we assessed the impact of this assumption on false-positive diagnoses. METHOD: We estimated false positives by simulating cancellation data using a binomial model. Performance was summarised by the difference in left and right cancelled targets (R-L) and the Centre of Cancellation (CoC). Diagnosis was based on a fixed cutoff versus cutoffs adjusted for the total number of cancelled targets and on single test performance versus unanimous or proportional agreement across multiple tests. Finally, we compared the simulation findings to empirical cancellation data acquired from 651 strokepatients. RESULTS: Using a fixed cutoff, the rate of false positives depended on the total number of cancelled targets and ranged from 10% to 30% for R-L scores and from 10% to 90% for CoC scores. The rate of false positives increased even further when diagnosis was based on proportional agreement across multiple tests. Adjusted cutoffs and unanimous agreement across multiple tests were effective at controlling false positives. For empirical data, fixed versus adjusted cutoffs differ in estimation of neglect prevalence by 13%, and this difference was largest for patients with non-spatial impairments. CONCLUSIONS: Our findings demonstrate the importance of considering non-spatial impairments when diagnosing neglect based on cancellation performance.