ObjectiveTo compare changes in T2 relaxation on magnetic resonance (MR) images of knee articular cartilage in younger and older amateur athletes before and after running.Materials and MethodsBy using a 3.0-T MR imager, quantitative T2 maps of weight-bearing femoral and tibial articular cartilages in 10 younger and 10 older amateur athletes were acquired before, immediately after, and 2 hours after 30 minutes of running. Changes in global cartilage T2 signals of the medial and lateral condyles of the femur and tibia and regional cartilage T2 signals in the medial condyles of femoral and tibia in response to exercise were compared between the two age groups.ResultsChanges in global cartilage T2 values after running did not differ significantly between the age groups. In terms of the depth variation, relatively higher T2 values in the older group than in the younger group were observed mainly in the superficial layers of the femoral and tibial cartilage (p < 0.05).ConclusionAge-related cartilage changes may occur mainly in the superficial layer of cartilage where collagen matrix degeneration is primarily initiated. However, no trend is observed regarding a global T2 changes between the younger and older age groups in response to exercise.

The Bradley-Terry model is widely used for analysis of pairwise preference data. We explain that the popularity of Bradley-Terry model is gained due to not only easy computation but also some nice asymptotic properties when the model is misspecified. For information retrieval required to analyze big ranking data, we propose to use a pseudo likelihood based on the Bradley-Terry model even when the true model is different from the Bradley-Terry model. We justify using the Bradley-Terry model by proving that the estimated ranking based on the proposed pseudo likelihood is consistent when the true model belongs to the class of Thurstone models, which is much bigger than the Bradley-Terry model.

Epidemiologic studies frequently try to estimate the impact of a specific risk factor. The risk difference and the risk ratio are generally useful measurements for this purpose. When using such measurements for rare events, the standard approaches based on the normal approximation may fail, in particular when no events are observed. In this paper, we discuss and evaluate several existing methods to construct confidence intervals around risk differences and risk ratios using Monte-Carlo simulations when the disease of interest is rare. The results in this paper provide guidance how to construct interval estimates of the risk differences and the risk ratios when no events are detected.

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