OUTCOME: Length of hospital stay ANALYSIS: primary_all_designs STUDIES: 5 TREATMENTS: Conventional, Retzius, Ultra Original data: treat1 treat2 TE seTE Beyatli_2025 Retzius Ultra 0.2000 0.0806 Elliott_2023 Conventional Retzius 0.1000 0.0959 Karsiyakali_2022 Conventional Retzius -0.0010 0.0981 Lin_2025 Conventional Ultra -1.1000 0.5852 Nagoya_RS_anatomy_OAB Conventional Retzius -1.0000 0.7030 Number of treatment arms (by study): narms Beyatli_2025 2 Elliott_2023 2 Karsiyakali_2022 2 Lin_2025 2 Nagoya_RS_anatomy_OAB 2 Results (random effects model): treat1 treat2 MD 95%-CI Beyatli_2025 Retzius Ultra -0.0790 [-0.7947; 0.6367] Elliott_2023 Conventional Retzius -0.1988 [-0.7157; 0.3181] Karsiyakali_2022 Conventional Retzius -0.1988 [-0.7157; 0.3181] Lin_2025 Conventional Ultra -0.2778 [-1.0803; 0.5247] Nagoya_RS_anatomy_OAB Conventional Retzius -0.1988 [-0.7157; 0.3181] Number of studies: k = 5 Number of pairwise comparisons: m = 5 Number of observations: o = 581 Number of treatments: n = 3 Number of designs: d = 3 Random effects model Treatment estimate (sm = 'MD', comparison: other treatments vs 'Conventional'): MD 95%-CI z p-value Conventional . . . . Retzius 0.1988 [-0.3181; 0.7157] 0.75 0.4510 Ultra 0.2778 [-0.5247; 1.0803] 0.68 0.4975 Quantifying heterogeneity / inconsistency: tau^2 = 0.1661; tau = 0.4076; I^2 = 61.7% [0.0%; 87.2%] Tests of heterogeneity (within designs) and inconsistency (between designs): Q d.f. p-value Total 7.84 3 0.0495 Within designs 2.75 2 0.2522 Between designs 5.08 1 0.0242 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 0.7629 Retzius 0.4056 Ultra 0.3315 --- SUCRA RANKING --- SUCRA Conventional 0.7632 Retzius 0.4048 Ultra 0.3320 - based on 20000 simulations --- GLOBAL INCONSISTENCY: DESIGN-BY-TREATMENT --- Q statistics to assess homogeneity / consistency Q df p-value Total 7.84 3 0.0495 Within designs 2.75 2 0.2522 Between designs 5.08 1 0.0242 Design-specific decomposition of within-designs Q statistic Design Q df p-value Conventional:Retzius 2.75 2 0.2522 Between-designs Q statistic after detaching of single designs (influential designs have p-value markedly different from 0.0242) Detached design Q df p-value Conventional:Retzius 0.00 0 -- Conventional:Ultra 0.00 0 -- Retzius:Ultra 0.00 0 -- 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 4.79 1 0.0286 0.0831 0.0069 --- LOCAL INCONSISTENCY: NODE SPLITTING/SIDE --- Separate indirect from direct evidence (SIDE) using back-calculation method Random effects model: comparison k prop nma direct indir. Diff z p-value Retzius:Conventional 3 0.90 0.1988 0.0736 1.3000 -1.2264 -1.41 0.1591 Ultra:Conventional 1 0.33 0.2778 1.1000 -0.1264 1.2264 1.41 0.1591 Retzius:Ultra 1 0.77 -0.0790 0.2000 -1.0264 1.2264 1.41 0.1591 Legend: comparison - Treatment comparison k - Number of studies providing direct evidence prop - Direct evidence proportion nma - Estimated treatment effect (MD) in network meta-analysis direct - Estimated treatment effect (MD) derived from direct evidence indir. - Estimated treatment effect (MD) derived from indirect evidence Diff - Difference between direct and indirect treatment estimates z - z-value of test for disagreement (direct versus indirect) p-value - p-value of test for disagreement (direct versus indirect)