https://github.com/ucl/metafloat
Estimation of covariate interactions and subgroup-specific treatment effects in meta-analysis.
https://github.com/ucl/metafloat
meta-analysis randomized-controlled-trials stata subgroups
Last synced: 5 months ago
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Estimation of covariate interactions and subgroup-specific treatment effects in meta-analysis.
- Host: GitHub
- URL: https://github.com/ucl/metafloat
- Owner: UCL
- License: gpl-3.0
- Created: 2021-09-30T13:08:16.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-06-18T14:35:41.000Z (over 1 year ago)
- Last Synced: 2024-06-18T17:56:32.405Z (over 1 year ago)
- Topics: meta-analysis, randomized-controlled-trials, stata, subgroups
- Language: Stata
- Homepage: https://doi.org/10.1002/jrsm.1590
- Size: 289 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# metafloat
v0.15 30aug2023
# A Stata package to estimate covariate interactions and subgroup-specific treatment effects in meta-analysis
Meta-analysis is a statistical technique for combining results from multiple independent studies, with the aim of estimating a single overall effect. However, it can also be used for assessing how treatment effects vary across participant subgroups. Ideally, this assessment will be based on treatment-covariate interaction effects derived _within each trial separately_, so that the pooled effects are free from aggregation bias. In particular, `metafloat` provides a simple way of estimating pooled interactions and "floating" subgroup-specific treatment effects from a set of observed (published, or otherwise pre-aggregated) treatment effects by trial and by subgroup.
See [Godolphin et al (2023)](https://doi.org/10.1002/jrsm.1590) for more information on the underlying methodology. The lead author, Dr Godolphin, also gave a [Cochrane webinar](https://training.cochrane.org/resource/estimating-interactions-and-subgroup-effects-in-aggregate-data-meta-analysis) on the methodology in January 2024.
# Installation
To install the package directly from GitHub, type from within Stata:
. net describe metafloat, from("https://raw.githubusercontent.com/UCL/metafloat/master/src/")
# Usage and documentation
Currently, documentation on usage and options may be found in the documentation files within Stata. After installation, type in Stata:
. help metafloat
# Example
A "two-panel" subgroup and interaction forest plot, as proposed by [Godolphin et al. (2023)](https://doi.org/10.1002/jrsm.1590)
Data taken from Supplementary Appendix 2, and displayed in Figure 1, of Godolphin et al (as above).
Data originally presented in Supplement 2, page 14, of [Shankar-Hari et al. (2021)](https://doi.org/10.1001/jama.2021.11330).
Note that this same example is used to demonstrate the `metan` and `forestplot` commands within the [metan GitHub pages](https://github.com/UCL/metan/main/Examples/Example5.md), but without using `metafloat` directly (and without generating subgroup-specific treatment effects free from aggregation bias). It may be seen that far more lines of code are needed.
To run this example in full, the `forestplot` command is required. This command is part of the `metan` [package](https://github.com/UCL/metan), which may be installed by typing:
. net describe metan, from("https://raw.githubusercontent.com/UCL/metan/master/src/")
```Stata
* Example generated by -dataex-. For more info, type help dataex
clear
input str17 TrialName byte Subgroup int(n0 e0 n1 e1)
"ARCHITECTS" 0 0 0 1 0
"ARCHITECTS" 1 11 2 9 0
"BACC-Bay" 0 81 4 158 9
"BACC-Bay" 1 1 0 3 0
"CORIMUNO-TOCI-1" 0 55 5 53 6
"CORIMUNO-TOCI-1" 1 12 3 10 1
"CORIMUNO-TOCI-ICU" 0 39 8 41 4
"CORIMUNO-TOCI-ICU" 1 4 2 8 4
"COV-AID" 0 30 4 33 3
"COV-AID" 1 42 3 48 6
"COVACTA" 0 103 16 238 44
"COVACTA" 1 41 12 56 14
"COVIDOSE2-SS-A" 0 6 1 13 0
"COVIDOSE2-SS-A" 1 2 1 6 0
"EMPACTA" 0 16 0 49 2
"EMPACTA" 1 112 11 200 24
"HMO-020-0224" 0 2 0 6 1
"HMO-020-0224" 1 15 8 31 10
"ImmCoVA" 0 1 0 1 0
"ImmCoVA" 1 26 2 21 2
"PreToVid" 0 11 2 11 1
"PreToVid" 1 167 32 159 20
"RECOVERY" 0 367 127 357 139
"RECOVERY" 1 1721 600 1664 482
"REMAP-CAP" 0 129 41 127 30
"REMAP-CAP" 1 217 73 214 53
"REMDACTA" 0 29 2 72 9
"REMDACTA" 1 181 39 358 69
"TOCIBRAS" 0 30 1 34 6
"TOCIBRAS" 1 34 5 31 8
end
generate long f1 = n1 - e1
generate long f0 = n0 - e0
quietly metan e1 f1 e0 f0, or nogr keeporder
label variable Trial Trial
label variable Subgroup `""Corticosteroid use""'
metafloat _ES _seES, study(Trial) subgroup(Subgroup) fixed clear keepvars(e1 n1 e0 n0)
gen counts0 = strofreal(cond(missing(e0), 0, e0)) + "/" + strofreal(cond(missing(n0), 0, n0)) if inlist(_y_USE, 1, 2)
gen counts1 = strofreal(cond(missing(e1), 0, e1)) + "/" + strofreal(cond(missing(n1), 0, n1)) if inlist(_y_USE, 1, 2)
label variable counts0 `""Usual care" "n/N""'
label variable counts1 `""Tocilizumab" "n/N""'
forestplot, prefix(_y) labels(_LABELS) or nowt lcols(counts0 counts1) keepall ///
favours("Favours tocilizumab" " " # "Favours usual care" " ") ///
xlabel(.125 "0.125" 1 8) range(.125 8) cirange(.2 8) boxsca(50) texts(150) astext(50) savedims(A) graphregion(color(white)) scheme(s2color) name(left)
forestplot, prefix(_yInt) interaction usedims(A) nonames eform effect("Interact. Odds Ratio") keepall nowt ///
favours("Favours greater effect of tocilizumab" "with corticosteroids" ///
# "Favours greater effect of tocilizumab" "without corticosteroids", fp(8)) ///
xlabel(.125 "0.125" 1 8) range(.125 8) cirange(.2 8) texts(150) graphregion(color(white)) scheme(s2color) name(right)
graph combine left right, imargin(zero) xsize(4.5) graphregion(color(white)) scheme(s2color)
```