Nonlinear mixedeffects modeling of MNREAD data

PURPOSE. It is often difficult to estimate parameters from indi-vidual clinical data because of noisy or incomplete measure-ments. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME by analyzing data from the MNREAD, a continuous-text read-ing-acuity chart. METHODS. The authors analyzed MNREAD data (measurements of reading speed vs. print size) for two groups: 42 adult ob-servers with normal vision and 14 patients with age-related macular degeneration (AMD). Truncated sets of MNREAD data were generated from the individual observers with normal vision. The MNREAD data were fitted with a two-limb function and an exponential-decay function using an individual curve