Dataset - Comparative Effectiveness of Surgical and Combined Interventions for Mandibular Angle Fractures: A Frequentist Network Meta-Analysis

This dataset was curated to support CINeMA assessments and complementary network meta-analysis for two subnetworks (“M” and “S”). Data were provided in two CSV files (cinema_M.csv, cinema_S.csv) at the study–arm level, including study and arm identifiers, treatment codes, numbers of responders and total participants, a risk-of-bias code (1 = low, 2 = some concerns, 3 = high), and an indicator of indirectness when available.

For the M subnetwork, evidence was assembled from 7 studies and 44 arms across 5 treatments, comprising 647 participants and 639 responders. Arm-level risk of bias was distributed as 13.6% low, 72.7% some concerns, and 13.6% high. The most frequently observed direct comparison was M1S versus M3D, followed by M1S versus M2 and M1L versus M1S. For the S subnetwork, 13 studies and 63 arms across 7 treatments were included, totaling 2,107 participants and 1,986 responders. The risk-of-bias profile indicated 71.4% low and 28.6% some concerns, with no high-risk arms. The most common direct comparison involved S1S versus S2, with additional emphasis on S1L versus S1S and S1L versus S3D.

Beyond risk-of-bias profiling, the network geometry was characterized to describe the distribution of evidence across treatments and comparisons, revealing centrally connected interventions with comparatively larger information contributions and peripheral nodes with sparser evidence. Random-effects network meta-analysis models were fitted to estimate pooled relative effects, and between-study heterogeneity was quantified to reflect variability beyond sampling error. Consistency was examined using global and local approaches (including design-by-treatment evaluations and node-splitting where appropriate) to assess agreement between direct and indirect evidence. Small-study effects were explored with comparison-adjusted and network funnel plots. Treatment ranking probabilities were estimated and summarized using SUCRA to communicate the relative standing of interventions under model assumptions. Sensitivity analyses were conducted to assess robustness, including the exclusion of high risk-of-bias studies, alternative assumptions for heterogeneity, and restriction to adequately informed contrasts.

Overall, the evidence base in both subnetworks was predominantly characterized by low to moderate risk of bias, with the principal methodological concern concentrated in the M subnetwork for the M1S versus M3D contrast due to the presence of high and moderate risk contributions. The S subnetwork displayed a more favorable profile without high-risk arms. The dataset enabled reproducible derivation of network structures, risk-of-bias summaries by treatment and comparison, model-based effect estimates, consistency diagnostics, ranking summaries, and robustness checks, supported by accompanying figures that documented network structure and risk-of-bias distributions.