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Table 6 Studentized residuals and PRESS residuals

From: Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models

Design Observation Treatment Model for heterogeneity
G.S.T fixed G.S.T random G.S.T dropped
    PRESS residual Studentized residual PRESS residual Studentized residual PRESS residual Studentized residual
1 1 acar 0.0545 0.2443 0.0785 0.1453 0.0642 0.2925
  2 plac -0.0545 -0.2443 -0.0785 -0.1453 -0.0642 -0.2925
2 3 acar -0.0234 -0.1022 0.0619 0.1056 -0.0259 -0.1142
  4 SUal 0.0234 0.1022 -0.0619 -0.1056 0.0259 0.1142
3 5 benf       
  6 plac . . . . . .
4 7 metf 0.0547 0.3026 -0.0781 -0.2282 -0.0814 -0.6783
  8 plac -0.0547 -0.3026 0.0781 0.2282 0.0814 0.6783
5 9 acar -0.0894 -0.2408 -0.1507 -0.2601 -0.1137 -0.3070
  10 metf -0.0276 -0.0930 0.0036 0.0075 0.0060 0.0205
  11 plac 0.1359 0.3615 0.1193 0.2273 0.1057 0.2833
6 12 metf 0.6807 3.6726 0.6095 1.1614 0.6910 3.8755
  13 SUal -0.6807 -3.6726 -0.6095 -1.1614 -0.6910 -3.8755
7 14 migl . . . . . .
  15 plac . . . . . .
8 16 piog -0.4337 -2.5934 -0.2802 -0.5585 -0.3638 -2.2987
  17 plac 0.4337 2.5934 0.2802 0.5585 0.3638 2.2987
9 18 metf -0.4719 -2.9147 -0.2927 -0.5779 -0.3467 -2.3246
  19 piog 0.4719 2.9147 0.2927 0.5779 0.3467 2.3246
10 20 piog -0.1074 -0.5173 -0.0073 -0.0141 -0.0445 -0.2173
  21 rosi 0.1074 0.5173 0.0073 0.0141 0.0445 0.2173
11 22 plac -0.2802 -1.9593 -0.2100 -0.6391 -0.3181 -2.4974
  23 rosi 0.2802 1.9593 0.2100 0.6391 0.3181 2.4974
12 24 metf -0.1105 -0.5920 -0.0616 -0.1610 -0.0179 -0.1005
  25 rosi 0.1105 0.5920 0.0616 0.1610 0.0179 0.1005
13 26 rosi -0.7077 -3.7022 -0.6733 -1.2693 -0.7424 -3.9701
  27 SUal 0.7077 3.7022 0.6733 1.2693 0.7424 3.9701
14 28 plac . . . . . .
  29 sita . . . . . .
15 30 plac . . . . . .
  31 vild . . . . . .
  1. Residuals for diabetes example of Senn et al. [7] were obtained by fitting the model G + T to design × treatment means computed from model (2) with different assumptions regarding the effect for heterogeneity (G.S.T).