Choose the effect sizes you plan to compute and the statistics you expect to extract. metaConvert assembles the exact spreadsheet columns you'll need - watch the sheet build itself as you go, so it's ready before you open the first study.
Plan this at the protocol stage - you don't need the studies in hand yet. Choose the effect sizes you'll compute and the statistics you expect to collect; your sheet builds itself on the right as you go.
Pick a measure to show only the data sources that can produce it. Optional - you can also just browse every source below.
| Predictor | Outcome | Effect size measure | Acronym |
|---|---|---|---|
| Binary | Continuous | Standardized mean difference | SMD |
| Binary | Continuous | Mean difference | MD |
| Binary | Continuous | Variability ratio / CoV | VAR |
| Continuous | Continuous | Correlation (r / Fisher's z) | COR |
| Binary | Binary | Odds ratio | OR |
| Binary | Binary | Risk ratio | RR |
| Binary | Binary | Number needed to treat | NNT |
| Time | Binary | Incidence rate ratio | IRR |
Clear the search or filters to see all options.
A preview of your sheet with realistic sample values. Each row is one study - replace the samples with your data.
reverse_*
Adds a TRUE/FALSE column per statistic so you can flip a study whose outcome runs the opposite way - keeping every effect size pointing the same direction.
reverse_* blank. But one trial scored the scale so higher = worse: put TRUE in that row's reverse_md (or whichever statistic you entered) and metaConvert flips its sign, so it pools in the same direction as the rest.
Untick any column you won't fill - study_id stays, it links your rows. The preview updates as you change them.