Introduction: Administrative health databases are important resources for population-based chronic disease research and surveillance, but case definitions to identify disease cases can have low sensitivity and specificity. Case definitions constructed using repeated measurements of diagnoses over time have potential for improved accuracy. In particular, dynamic classification, which uses probabilistic models to classify individuals into disease, non-disease, and indeterminate categories and then updates these classifications as new information becomes available, are promising alternatives to conventional static classification methods for case ascertainment. Purpose & Objectives: The research purpose was to develop and evaluate methods that use longitudinal diagnostic information from population-based administrative databases to improve the accuracy of chronic disease case definitions. The objectives were to: (1) develop dynamic classification methods for retrospective longitudinal administrative data, (2) apply and validate dynamic classification to identify cases of juvenile arthritis (JA), and (3) compare the performance of case definitions developed using dynamic classification with case definitions developed using static approaches for classification. Methods: Dynamic longitudinal discriminant analysis (LoDA) was adapted for retrospective longitudinal administrative data and applied to administrative health data from Manitoba to identify cases of JA. The Pediatric Rheumatology Clinical Database was used for validation. Performance of the JA case definitions constructed with dynamic LoDA was measured using sensitivity, specificity, positive predictive value (PPV), and mean time to classification. Classification accuracy was compared for dynamic LoDA, deterministic case definitions and static LoDA models. Results: The study cohort included 797 children from the clinical database who could be linked to administrative health data from birth to age 16; 386 (48.4%) were JA cases and 411 (51.6%) were non-cases. The dynamic LoDA model with the best fit used a longitudinal binary variable for any-JA related physician visit or hospitalization. It had sensitivity of 0.70, specificity of 0.81, PPV of 0.82, and left 2% of the cohort unclassified after all data were used. On average, it took 9.21 years of data to classify individuals as a JA case or non-case. Both the deterministic case definition and static LoDA model outperformed the best dynamic LoDA model. Conclusion: The results suggest that dynamic classification can produce accurate case definitions using longitudinal information from administrative health data, although the choice of methods and their comparative performance will depend on the characteristics of the disease.