Fixed-Effect Weighted Mean d Calculator
Fixed-effect inverse variance weighted mean study effect, caller independently established compatible study variance/effect metrics; not heterogeneity correction or bias removal. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A fixed-effect meta-analysis combines compatible study effects using inverse-variance weights.
A fixed-effect meta-analysis combines compatible study effects using inverse-variance weights.1
Its weighted mean does not estimate or correct between-study heterogeneity. The calculation and input requirements are explained below.
When to use Fixed-Effect Weighted Mean d Calculator
Enter nonempty aligned finite study effects and nonnegative independently established inverse-variance weights with positive sum.
How to use Fixed-Effect Weighted Mean d Calculator 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 Fixed-Effect Weighted Mean d Calculator.
- 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 |
|---|---|---|---|
| actualStudyEffects | Actual Study Effects. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
| callerActualInverseVarianceWeights | Caller Actual Inverse Variance Weights. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter nonempty aligned finite study effects and nonnegative independently established inverse-variance weights with positive sum. Return the weighted mean in the supplied effect metric. No weights, random-effects variance or bias correction are estimated.
fixedEffectMean=sum(effect*weight)/sum(weights) 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": {
"actualStudyEffects": [
1,
3
],
"callerActualInverseVarianceWeights": [
1,
1
]
},
"output": {
"result": {
"value": 2
}
}
}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 Logit Method OR to d Converter for a complementary operation: A logistic odds ratio can be mapped approximately to standardized d under a latent-distribution assumption.
Use Hedges g From t Statistic Converter for a complementary operation: Hedges' g adjusts an independent-group standardized contrast for sample-size bias.
References
-
Fixed-Effect Weighted Mean d Calculator — tool.io
Similar or alternative tools
- Logit Method OR to d Converter
A logistic odds ratio can be mapped approximately to standardized d under a latent-distribution assumption.
- Hedges g From t Statistic Converter
Hedges' g adjusts an independent-group standardized contrast for sample-size bias.