Provides a standard way to report statistical evidence. Works across clinical and biomedical analyses. Enables results to be compared, stored, and reused across studies.
Evidence Ratio defines how evidence strength is reported, independent of how analyses are performed.
Evidence ratio adopts a shared evidence standard. Foundational tools must be unrestricted so results can move safely between laboratories, hospitals, and national programmes.
Read moreEvidence Ratio does not change how analyses are run. It standardises how their evidence is reported.
Installation
Until the package is available on CRAN, you can install the development version directly from GitHub.
Option 1: install from GitHub
install.packages("devtools")
devtools::install_github("DylanLawless/src-evidenceratio_package")
Option 2: install from a local clone
If you have cloned the repository locally:
install.packages("devtools")
devtools::install("path/to/evidenceratio_package", upgrade = "never", clean = TRUE)
Verify installation
library(evidenceratio)
help(package = "evidenceratio")
What Evidence Ratio does
Clinical trials and biomedical studies analyse heterogeneous endpoints using appropriate statistical models. These analyses are methodologically sound, but their results are reported in incompatible formats.
Evidence Ratio introduces a single likelihood based evidence scale that accompanies existing effect estimates and uncertainty intervals. This scale behaves consistently across outcomes, models, and studies.
The result is a shared evidence unit that travels cleanly between trials, endpoints, databases, and institutions.
What Evidence Ratio does not do
Evidence Ratio does not replace statistical models.
It does not define decision thresholds.
It does not assess clinical or practical importance.
It does not perform multiplicity correction.
It does not replace effect sizes or uncertainty intervals.
Evidence Ratio operates only at the level of reporting.
Conceptual role
Each statistical result contains three distinct components.
Magnitude is captured by the effect estimate.
Precision is captured by the uncertainty interval.
Evidence is captured by a likelihood ratio comparing an explicit effect model with a no effect model.
Evidence Ratio formalises this evidence component and reports it on a common log10 scale. Existing analyses remain unchanged.
Why this streamlines clinical trials
Clinical trials routinely report multiple endpoints analysed using different models. These results are difficult to compare side by side. Evidence Ratio gives every endpoint the same evidence unit. This allows heterogeneous results to be aligned in a single table, forest plot, dashboard, or database. This supports multi endpoint trials, platform trials, meta analysis, regulatory review, and secondary data reuse.
Example output
Each result is reported using the same three quantities.
| Effect estimate | Uncertainty interval (95 percent) | log10 evidence ratio |
|---|---|---|
| −1.18 | [−1.62, −0.74] | 12.87 |
The effect and interval remain on their native scale. The evidence ratio provides a directly comparable measure of evidence support.
Unified reporting across analyses
| Analysis type | Effect estimate | Uncertainty interval (95 percent) | log10 E(x) |
|---|---|---|---|
| One sample mean test | 0.42 | [0.21, 0.63] | 2.91 |
| Two sample mean test | −1.18 | [−1.62, −0.74] | 12.87 |
| Binary outcome association | 0.36 | [−0.22, 0.94] | 0.44 |
| Linear regression | −0.51 | [−1.19, 0.17] | 0.24 |
| Regression coefficient | −5.41 | [−6.18, −4.63] | 16.95 |
| Time to event analysis | −0.22 | [−0.52, 0.08] | 0.58 |
| Survival analysis | 0.88 | [0.61, 1.15] | 6.82 |
Different outcome types retain their native effect measures while sharing a common evidence scale.
Method overview
The evidence ratio is defined as a likelihood ratio comparing an effect model with a no effect model. It is reported on the log10 scale. Under the null model, large evidence values are rare. This provides finite sample validity without introducing decision thresholds. Further details are provided in the accompanying manuscript.
Standards and implementation
Evidence Ratio is implemented in the evidenceratio R package.
It conforms to the Evidence Ratio Reporting Standard (SGA-ERRS-1.0), published by the Swiss Genomics Association.
View the ERRS standard
