PURPOSE: The purpose of this study was to determine whether combining a structural measure with contrast sensitivity perimetry (CSP), which has lower test-retest variability than static automated perimetry (SAP), reduces prediction error with 2 models of glaucoma progression. METHODS: In this retrospective analysis, eyes with 5 visits with rim area (RA), SAP, and CSP measures were selected from 2 datasets. Twenty-six eyes with open-angle glaucoma were included in the analyses. For CSP and SAP, mean sensitivity (MS) was obtained by converting the sensitivity values at each location from decibel (SAP) or log units (CSP) to linear units, and then averaging all values. MS and RA values were expressed as percent of mean normal based on independent normative data. Data from the first 3 and 4 visits were used to calculate errors in prediction for the fourth and fifth visits, respectively. Prediction errors were obtained for simple linear regression and the dynamic structure-function (DSF) model. RESULTS: With linear regression, the median prediction errors ranged from 6% to 17% when SAP MS and RA were used and from 9% to 17% when CSP MS and RA were used. With the DSF model, the median prediction errors ranged from 6% to 11% when SAP MS and RA were used and from 7% to 16% when CSP MS and RA were used. CONCLUSIONS: The DSF model had consistently lower prediction errors than simple linear regression. The lower test-retest variability of CSP in glaucomatous defects did not, however, result in lower prediction error.