1182 Nodule characteristics model approach is particularly appropriate for these types of data where correlations between different measurements The nodule characteristics (location and morphology) were within participants could be present [25]. In detail, we used determined by consensus double reading based on visual a three-level model accounting for correlations within analysis of 1.0 mm thick axial images. Based on location, participants, between nodules and repeat measures, and the nodules were classified into two subgroups: (1) purely accounting for correlations within nodules between intra-parenchymal, with little or no contact with the pleura, repeated measures. Consequently, the highest level consists vessels or fissures or (2) attached, including pleural-based, of the participants, the second level of the nodules and the fissure-attached and juxtavascular, with a pleura, fissure or lowest level of the repeated measures. vessel contact line larger than 50% of the nodule diameter. The reconstruction settings, nodule location and mor- Based on their morphology, nodules were classified into phology were used as independent variables. Both random two groups: smooth-round and non-smooth-round (includ- and fixed effects were considered. In the modelling ing polylobulated, spiculated and irregular nodules). process, variables were included in the model sequentially. Although attached and non-smooth-round nodules were After each step the goodness of fit was determined by the reported to have high odds ratios of measurement difference in deviance (-2 × log likelihood) between the variability, we decided to include them in order to be present and the previous model. The modelling started with able to evaluate the effect of reconstruction settings on an empty model, in which only a fixed intercept and three volume measurements for all nodule types encountered by random terms were modelled. Thereafter, reconstruction CT screening [15, 16]. setting, nodule characteristics and their interaction were subsequently added. For this multi-level analysis, the MLwiN software package for fitting multi-level models Statistical analysis was used (version 2.02; Centre for Multilevel Modelling, Institute of Education, University of London, UK). The measured volumes were logarithmically transformed (lnV) because of their skewed distribution. As the anti-log of differences on a log scale provides dimensionless ratios, the differences on the log scale were back-transformed to provide ratios among actual measurements. The ratios were further changed into relative volume difference (RVD, %) minus 100%. Repeatability of each setting As three repeated volume measurements of pulmonary nodules were performed by the same reader and the same software for each reconstruction setting, the mean difference between replicates is expected to be zero, implying that the backward transformed mean difference has a ratio of 100%. Thus, we calculated the repeatability coefficients according to the Bland and Altman method, which defined the value below which 95% of the differences will lie and thus a larger coefficient corresponds to a lower repeatability [24]. Repeatability coefficients were calculated with one way analysis of variance, defined as 2.77 times the within-subject standard deviation. Effect of setting and repeat measures on the measured volume A multi-level model approach was performed to get unbiased effect estimates of reconstruction settings on the measured volume. We used nested data in the sense that some persons had more than one nodule and within each nodule we had several measurements. The multi-level 1 2 3 4 5 6 7 9 Results Of 82 participants, the median number of nodules per participant was two and ranged from one to nine (Table 1). Of 200 nodules, 36% (71/200) were attached, and 33.5% (47/200) were non-smooth-round. Repeatability of each setting The plots of the standard deviation of three measurements for every nodule against their mean for three settings are illustrated in Fig. 1. The repeatability coefficients of lnV were 0.08, 0.20 and 0.32 for settings A, B and C respectively. After antilog transformation and presented as RVD, the coefficients were 8.9, 22.5 and 37.5% for the three settings respectively. Non-smooth-round, attached nodules had a lower repeatability than smooth-round and Table 1 The distribution of 200 nodules in 82 participants Number of nodules per participants Number of participants (%) 34 (41) 18 (22) 11 (13) 9 (11) 4 (5) 3 (4) 2 (3) 1 (1)