Diagnostics are stress tests
Sensitivity and diagnostic analyses help reviewers understand how robust a result is to modeling choices, influential studies, study order, risk-of-bias exclusions, and small-study effect patterns. They do not prove that the main analysis is correct.
A stable leave-one-out result can still be based on the wrong effect measure. A funnel plot can look symmetric in an underpowered dataset. A significant asymmetry test can reflect heterogeneity, outcome definition differences, study quality, chance, or publication bias.
Leave-one-out analysis
Leave-one-out analysis refits the same meta-analysis k times, each time removing one study. If the main pooled estimate is y_bar and the estimate without study j is y_bar(-j), the diagnostic question is whether y_bar(-j), its interval, tau^2, or I^2 changes enough to alter interpretation.
Coventra uses the same selected effect measure and model settings for each refit. The check is therefore a sensitivity analysis for influential studies, not a correction for poor extraction or inappropriate model choice.
Influence diagnostics
Influence diagnostics examine how much each study contributes to model fit, heterogeneity, and the pooled estimate. Typical diagnostics include externally standardized residuals, hat values, Cook-like distances, covariance ratios, and contribution to Q, depending on the fitted model and package support.
These outputs should be interpreted alongside study design, risk of bias, population, intervention dose, outcome definition, and follow-up. If an influential study differs in population, dose, measurement, risk of bias, or design, the review should discuss that difference rather than only reporting a numeric influence statistic.
Cumulative meta-analysis
Cumulative meta-analysis adds studies one at a time in a chosen order and refits the model after each addition. If studies are ordered by year, the result can show how the evidence base evolved over time.
Recommended cumulative ordering uses year when available. Users can choose other supported ordering fields such as sample size, effect, or label. An arbitrary row order should not be interpreted as evidence accumulation.
Changeable diagnostic settings
- Excluded study IDs and subgroup filters can be applied before diagnostics run.
- Cumulative sort field and sort direction are user-changeable.
- Plot options follow the same display controls used by the main forest plot.
- Effect measure and model settings should be inherited from the main planned analysis so diagnostics answer the same question.
- Users can change which bias tests run and whether trim-and-fill is applied.
What a funnel plot displays
A funnel plot places study effect estimates on one axis and a precision measure, commonly standard error or inverse standard error, on the other. In the absence of small-study effects and with compatible assumptions, less precise studies should scatter more widely while more precise studies cluster near the pooled estimate.
Coventra renders funnel plots from the same fitted meta-analysis object used for the selected outcome. Contour guides are enabled by default in plot options so reviewers can inspect whether apparent missing areas align with statistical-significance regions.
Default app behavior
- Publication-bias analysis needs at least 3 studies to render, and Coventra warns that formal tests are exploratory with fewer than 10 studies.
- By default, Coventra runs Egger, Begg, and Peters tests when the data support them.
- Trim-and-fill runs by default for pairwise funnel workflows.
- Contour guides are enabled by default in plot options.
- Single-arm proportion and rate funnels include an extra warning because asymmetry can reflect prevalence, incidence, or study-size relationships rather than publication bias.
Asymmetry tests
Egger-style testing is commonly described as a regression of a standardized effect on precision. A simplified form is z_i = y_i / SE_i, regressed against precision_i = 1 / SE_i; a non-zero intercept suggests asymmetry. The exact implementation depends on the package and effect measure.
Begg-style testing uses rank correlation between effect estimates and their variances or standard errors. Peters-style approaches are used in some binary-outcome settings to reduce artifact from effect-size and SE correlation.
Cochrane guidance cautions that tests for funnel plot asymmetry generally have low power and should usually be used only when there are at least 10 studies. Some tests, including the original Egger test, are not recommended for odds ratios and SMDs because effect estimates can be mechanically correlated with their standard errors.
Funnel asymmetry is not a diagnosis of publication bias. It can reflect true heterogeneity, poor methodological quality, selective reporting, chance, scale artifacts, or inappropriate effect measures.
Trim-and-fill
Trim-and-fill estimates how many studies might be missing to make a funnel plot more symmetric, then recalculates an adjusted pooled estimate after imputing mirror-image studies. The method is a sensitivity analysis, not a bias correction that restores truth.
Coventra reports the number of filled studies and the adjusted random-effects estimate where the R service can calculate it. Reviewers should compare the original and adjusted estimates and decide whether the result is robust to a plausible missing-evidence pattern.
Reporting checklist
- Run diagnostics only after the main effect-size and variance choices are verified.
- Report whether leave-one-out changed the direction, magnitude, significance, or certainty of the main result.
- Identify influential studies by name and explain the clinical or methodological reason they may differ.
- Report the ordering used for cumulative meta-analysis.
- Report the number of included studies before interpreting any asymmetry test.
- Report which asymmetry tests were run and whether they are appropriate for the effect measure.
- Interpret visual asymmetry with the contour-enhanced plot and clinical heterogeneity.
- Report trim-and-fill as exploratory sensitivity output, not as corrected truth.
- Avoid saying there is no publication bias only because a test was non-significant.
- Report unavailable diagnostics when the data structure does not support them.