Network meta-interpolation

Plots for NMI using subgroup analyses

Network meta-interpolation (Harari and colleagues, 2023) uses the subgroup results that trials routinely publish to estimate how treatment effects change with effect modifiers, study by study, and interpolates each trial’s effect to a common set of effect-modifier values before running a standard NMA. Unlike most meta-regression approaches, it does not assume that effect modification is shared across treatments. Its graphics make the interpolation and the data it depends on visible.

Minimum graphical set

All plots for NMI

Effect display

Estimates, intervals, and pooled summaries: the forest plot and its many descendants.

Subgroup forest plot
A forest plot split by a categorical moderator, with a pooled estimate per subgroup and a test for subgroup differences.
MANMANMIML-NMR
Method-comparison forest plot
The same treatment comparison estimated by several indirect comparison methods, stacked as forest rows, often against a known truth.
ITCMAICSTCML-NMRNMIML-UMR

Network geometry and evidence flow

Which treatments are compared directly, how much evidence each comparison carries, and where it flows.

Network graph
Treatments as nodes and direct randomized comparisons as edges, showing how the evidence base is connected.
NMAML-NMRNMI
Transitivity plot
The distribution of potential effect modifiers across trials or direct comparisons, to judge whether indirect comparisons are plausible.
NMAML-NMRNMIITC

Weighting, balance, and overlap

MAIC diagnostics: what the weights did, how much information survived, and whether populations overlap.

Love plot
Standardized differences between the IPD and the comparator population for each covariate, before and after weighting.
MAICSTCML-NMRNMIML-UMR

Outcome regression and transportability

STC and G-computation diagnostics: functional form, extrapolation, and effects in a target population.

Marginal effect by target population
The marginal treatment effect standardized to several target populations, showing that with effect modification the effect is a property of the population.
STCML-NMRNMIML-UMR

Multilevel network meta-regression

ML-NMR graphics for mixed IPD and aggregate networks, numerical integration, and population-specific effects.

Effect-modifier curve
The conditional treatment effect against a covariate for each treatment, with the observed IPD and aggregate covariate values marked.
ML-NMRSTCNMIML-UMR

Network meta-interpolation

NMI graphics built on subgroup evidence and interpolation to a common effect-modifier value.

NMI interpolation plot
Each trial's overall and subgroup estimates plotted against effect-modifier prevalence, with lines interpolating every trial to a common target value.
NMI
Effect-modification comparison plot
The treatment effect across values of an effect modifier as implied by each synthesis method, compared with the truth in a simulation.
NMIML-NMRNMA
Subgroup data availability matrix
A trial-by-subgroup grid showing which overall and subgroup-specific estimates are reported, and whether they come with standard errors.
NMIML-NMR

Simulation and method evaluation

Graphics from the proof-of-concept literature that evaluates methods under known truth.

Nested loop plot
Performance of several methods across every scenario of a factorial simulation study, with scenarios ordered in nested loops and their factor levels drawn underneath.
MAICSTCML-NMRNMIML-UMR
Simulation performance plot
A performance measure for each method and scenario of a simulation study, with Monte Carlo uncertainty intervals.
MANMAMAICSTCML-NMRNMIML-UMR
Zip plot
Every replication's confidence interval, ranked by how far its estimate lies from the truth, showing which intervals fail to cover.
MANMAMAICSTCML-NMRNMIML-UMR

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