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Foreword: Statistics in the analysis of arthritis data

Arthritis is one of the prevalent chronic diseases that affects about 25.9 million women and 16.8 million men in America. It is an autoimmune disease such as rheumatoid arthritis (RA), ankylosing spondylitis, osteoarthritis, systemic lupus erythematosus (Lupus), scleroderma and fibromyalgia. This disease is a systemic disease that affects any part of the body and can lead to long-term damage in the target body system, severe pain and serious disability. Early diagnosis and treatment of arthritis is critical in limiting the disease activity and damage in the target body system, which, in turn, limit the loss of a patient’s daily activity, higher medical cost and potential surgery. In the development of an effective drug for such a chronic and systemic disease, there are some challenges that modern statistical techniques can help overcome. In the drug development research, the development of a ‘good’ measure of disease improvement is important. Due to the characteristics of systemic disease, the main difficulty in the analysis of arthritis studies is defining a single measure that reflects the progression or outcome of each disease. The research of RA uses a set of various measures, including joint counts, pain assessment, physician and patient global assessments, functional disability and acute phase reactants to evaluate the patient disease activity. The American College of Rheumatology 20% improvement criteria (ACR20), which is currently accepted as a composite measure of treatment effectiveness by the rheumatologic community, is a binary index that may be problematic when the distribution is skewed. It also has a problem not representing patient’s disease status at the end of treatment. Once the patients’ true state of disease is identified, more sophisticated statistical methods, such as the regression approach, the variance component method, discriminant approach or CART, can be used to develop a composite index that is easy to use, consistent over time, reproducible and sensitive to discriminate the treatment effect. The radiographic tools such as X-ray, MRI or CT are often used to assess the disease activity or damage in the joints or other body system. In the area of RA, the radiographic score and progression rate are important clinical measures used to classify the structural damage to joints. The current scoring methods of radiographic damage depend on the judgment or interpretation of a reader; so there are problems of relatively large intravariability within readers and inter-variability between readers within each study as well as the problem in the comparability of results across studies. Developing more objective and less variable radiographic scores or progression rates that are clinically meaningful is important. Since most patients do not progress or progress very little in the 6or 12-month periods in clinical trials, the comparison of the two groups results in the comparison of two extreme tails. The challenge then is shifted from comparing statistics of central location, which is commonly used to the comparison of real radiographic progression of the two groups. Other statistical approaches that compare two distributions, yet are robust to extreme values may be applied.