Skip to main content

Table 2 Estimate and 95% confidence interval for the effect of demographic features on the T2 MRI outcomes

From: Quantitative MRI analysis in children with multiple sclerosis: a multicenter feasibility pilot study

Factor Analysis Rate ratio for T2 lesion number* Log-transformed T2 lesion volume** Log-transformed maximum lesion volume** Log-transformed mean lesion size†
Age Unadjusted 1.00 (0.93, 1.07) P = 0.95 −0.10 (−0.22, 0.01) P = 0.08 −0.11 (−0.24, 0.01) P = 0.078 −0.02 (−0.06, 0.02) P = 0.266
  Adjusted 1.00 (0.93, 1.07) P = 0.99 −0.09 (−0.21, 0.03) P = 0.13 −0.10 (−0.23, 0.03) P = 0.12 −0.02 (−0.07, 0.02) P = 0.24
Gender (female/male) Unadjusted 1.16 (0.75, 1.80) P = 0.51 −0.30 (−1.08, 0.48) P = 0.45 −0.35 (−1.20, 0.50) P = 0.41 −0.24 (−0.52, 0.03) P = 0.08
Adjusted 1.24 (0.78, 1.96) P = 0.36 −0.22 (−1.08, 0.64) P = 0.61 −0.26 (−1.20, 0.67) P = 0.57 −0.26 (−0.55, 0.03) P = 0.07
Race (Non-white/White) Unadjusted 1.20 (0.76, 1.87) P = 0.44 0.87 (0.10, 1.64) P = 0.028 1.16 (0.34, 1.99)^ P = 0.0065 0.01 (−0.27, 0.30) P = 0.92
  Adjusted 1.13 (0.69, 1.86) P = 0.62 0.87 (0.03, 1.72) P = 0.044 1.19 (0.29, 2.08)^ P = 0.011 0.01 (−0.30, 0.31) P = 0.97
Ethnicity (Hispanic/Non-Hispanic) Unadjusted 1.39 (0.86, 2.22) P = 0.18 0.46 (−0.39, 1.32) P = 0.28 0.45 (−0.48, 1.39) P = 0.33 −0.14 (−0.43, 0.16) P = 0.36
  Adjusted 1.52 (0.86, 2.69) P = 0.15 0.36 (−0.68, 1.40) P = 0.49 0.25 (−0.89, 1.38) P = 0.66 −0.25 (−0.58, 0.09) P = 0.14
EDSS Unadjusted 1.04 (0.86, 1.27) P = 0.67 0.20 (−0.15, 0.56) P = 0.25 0.22 (−0.16, 0.60) P = 0.26 0.05 (−0.06, 0.17) P = 0.36
  Adjusted 1.01 (0.82, 1.23) P = 0.95 0.17 (−0.21, 0.55) P = 0.36 0.19 (−0.22, 0.61) P = 0.35 0.07 (−0.05, 0.19) P = 0.25
  1. Legend: *: negative binomial regression model, **: linear regression model, : repeated measures model, EDSS: expanded disability status scale, ^: p-value < 0.05. For negative binomial model, the rate ratio corresponds to a one-unit increase in age or EDSS and the difference between the groups for the other factors. For the linear regression model, the estimate is the increase in the log-transformed volume for a one-unit increase in age or EDSS and the difference between the groups for the other factors. Analyses were adjusted for disease duration and disease modifying therapy duration.