OUTCOME: Clavien-Dindo grade >=III complications 
ANALYSIS: primary_all_designs 
STUDIES: 7 
TREATMENTS: Conventional, Retzius, Ultra 

Original data:

                       treat1  treat2      TE   seTE
Beyatli_2025          Retzius   Ultra  0.0526 2.0053
Eden_2017        Conventional Retzius  0.7191 1.2458
Elliott_2023     Conventional Retzius -1.6642 1.5668
Karsiyakali_2022 Conventional Retzius -1.5609 0.8129
Lambert_2023     Conventional Retzius  1.6295 1.5557
Lim_2014         Conventional Retzius -0.0000 2.0099
Yilmaz_2023      Conventional Retzius -0.0000 0.5687

Number of treatment arms (by study):
                 narms
Beyatli_2025         2
Eden_2017            2
Elliott_2023         2
Karsiyakali_2022     2
Lambert_2023         2
Lim_2014             2
Yilmaz_2023          2

Results (random effects model):

                       treat1  treat2     OR            95%-CI
Beyatli_2025          Retzius   Ultra 1.0541 [0.0183; 60.8552]
Eden_2017        Conventional Retzius 0.7399 [0.2882;  1.8997]
Elliott_2023     Conventional Retzius 0.7399 [0.2882;  1.8997]
Karsiyakali_2022 Conventional Retzius 0.7399 [0.2882;  1.8997]
Lambert_2023     Conventional Retzius 0.7399 [0.2882;  1.8997]
Lim_2014         Conventional Retzius 0.7399 [0.2882;  1.8997]
Yilmaz_2023      Conventional Retzius 0.7399 [0.2882;  1.8997]

Number of studies: k = 7
Number of pairwise comparisons: m = 7
Number of observations: o = 1001
Number of treatments: n = 3
Number of designs: d = 2

Random effects model

Treatment estimate (sm = 'OR', comparison: other treatments vs 'Conventional'):
                 OR            95%-CI    z p-value
Conventional      .                 .    .       .
Retzius      1.3515 [0.5264;  3.4700] 0.63  0.5312
Ultra        1.2822 [0.0199; 82.4845] 0.12  0.9069

Quantifying heterogeneity / inconsistency:
tau^2 = 0.2611; tau = 0.5110; I^2 = 11.9% [0.0%; 77.6%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                   Q d.f. p-value
Total           5.67    5  0.3394
Within designs  5.67    5  0.3394
Between designs 0.00    0      --

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- Restricted maximum-likelihood estimator for tau^2
- Calculation of I^2 based on Q

--- P-SCORE RANKING ---
             P-score
Conventional  0.6405
Ultra         0.4818
Retzius       0.3777

--- SUCRA RANKING ---
              SUCRA
Conventional 0.6379
Ultra        0.4840
Retzius      0.3782

- based on 20000 simulations

--- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT ---
Q statistics to assess homogeneity / consistency

                   Q df p-value
Total           5.67  5  0.3394
Within designs  5.67  5  0.3394
Between designs 0.00  0      --

Design-specific decomposition of within-designs Q statistic

               Design    Q df p-value
 Conventional:Retzius 5.67  5  0.3394

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                   Q df p-value tau.within tau2.within
Between designs 0.00  0      --     0.3948      0.1559

--- LOCAL INCONSISTENCY: NODE SPLITTING/SIDE ---
Separate indirect from direct evidence (SIDE) using back-calculation method

Random effects model: 

           comparison k prop    nma direct indir. RoR z p-value
 Retzius:Conventional 6 1.00 1.3515 1.3515      .   . .       .
   Ultra:Conventional 0    0 1.2822      . 1.2822   . .       .
        Retzius:Ultra 1 1.00 1.0541 1.0541      .   . .       .

Legend:
 comparison - Treatment comparison
 k          - Number of studies providing direct evidence
 prop       - Direct evidence proportion
 nma        - Estimated treatment effect (OR) in network meta-analysis
 direct     - Estimated treatment effect (OR) derived from direct evidence
 indir.     - Estimated treatment effect (OR) derived from indirect evidence
 RoR        - Ratio of Ratios (direct versus indirect)
 z          - z-value of test for disagreement (direct versus indirect)
 p-value    - p-value of test for disagreement (direct versus indirect)