Idiopathic inflammatory myopathies are usually regarded as a heterogeneous group of autoimmune rheumatic diseases. Dermatomyositis and polymyositis may affect children and adults and, although rare, are a major cause of disability. In order to assess the value of conventional and newer therapies, a core set of measures for assessing myositis outcomes are being developed . This thesis reports on the design and analysis of two real patient exercises carried out to study proposed measures. An approach to the study of reliability and agreement is presented. Inference procedures for ratios of standard errors are developed. The myostitis measures are based on previous work in systemic lupus erythematosus (lupus), a major autoimmune rheumatic disease. International attempts to define validated disease activity and damage indices to assess patients with lupus have provided a consistent way to assess the disease. However, its multiple clinical manifestations prove a great challenge to rheumatologists managing patients with lupus. There is a need to better understand predictors of disease activity in order to improve and standardize therapy and to prevent the development of chronic damage. This thesis presents an analysis of a clinical database for patients with lupus. The aim is to develop approaches to examine the interrelationships between disease activity in the different organ systems. The database available for analysis consists of data collected on 440 patients over a period of 10 years. The analysis is based on logistic regression methodology with outcomes defined at the times of clinic visits. The usefulness of separate logistic regressions with dynamic covariates for the analysis of multinomial panel data is illustrated. The efficiency of the approach relative to modelling disease activity in continuous time is investigated.