Photodegradation, driven primarily by ultraviolet (UV) radiation, is the primary cause of failure for organic paints and coatings, as well as many other products made from polymeric materials exposed to sunlight. Traditional methods of service life prediction involve the use of outdoor exposure in harsh UV environments (e.g., Florida and Arizona). Such tests, however, require too much time (generally many years) to do an evaluation. To overcome the shortcomings of traditional methods, scientists at the U.S. National Institute of Standards and Technology (NIST) conducted a multiyear research program to collect necessary data via scientifically-based laboratory accelerated tests. This paper presents the statistical modeling and analysis of the photodegradation data collected at NIST, and predictions of degradation for outdoor specimens that are subjected to weathering. The analysis involves identifying a physics/chemistry-motivated model that will adequately describe photodegradation paths. The model incorporates the effects of explanatory variables which are UV spectrum, UV intensity, temperature, and relative humidity. We use a nonlinear mixed-effects model to describe the sample paths. We extend the model to allow for dynamic covariates and compare predictions with specimens that were exposed in an outdoor environment where the explanatory variables are uncontrolled but recorded. We also discuss the findings from the analysis of the NIST data and some areas for future research.