Logit Method OR to d Converter
Approximate logisticOR→standardizedd lnOR√3/πunder latent logistic assumption, not direct effect size estimate/causal equivalence. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A logistic odds ratio can be mapped approximately to standardized d under a latent-distribution assumption.
A logistic odds ratio can be mapped approximately to standardized d under a latent-distribution assumption.1
That transformation does not establish a causal effect. The calculation and input requirements are explained below.
When to use Logit Method OR to d Converter
Enter a positive odds ratio.
How to use Logit Method OR to d Converter and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Logit Method OR to d Converter.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualOddsRatio | Actual Odds Ratio. Use the units and matching context stated in the calculation below. | number | Required; strictly positive; finite; other constraints below |
Enter a positive odds ratio. Multiply its natural logarithm by sqrt(3)/pi; ratios below one yield negative d. This is the latent-logistic approximation.
approximateD=ln(oddsRatio)*sqrt(3)/pi Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"actualOddsRatio": 2
},
"output": {
"result": {
"value": 0.3821520694228441
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Fixed-Effect Weighted Mean d Calculator for a complementary operation: A fixed-effect meta-analysis combines compatible study effects using inverse-variance weights.
Use Hedges g From t Statistic Converter for a complementary operation: Hedges' g adjusts an independent-group standardized contrast for sample-size bias.
References
-
Logit Method OR to d Converter — tool.io
Similar or alternative tools
- Fixed-Effect Weighted Mean d Calculator
A fixed-effect meta-analysis combines compatible study effects using inverse-variance weights.
- Hedges g From t Statistic Converter
Hedges' g adjusts an independent-group standardized contrast for sample-size bias.