Simulated individual patient data for the index treatment (arthroscopic
subacromial decompression, ASD) in a continuous-outcome ML-UMR example. Pair
with shoulder_agd (the comparator). Not real patient data.
Format
A data frame with 147 rows and 7 columns:
- study
study label (
"FIMPACT")- treatment
index treatment label (
"ASD")- subject
row identifier
- age
age in years
- sex
1 = male, 0 = female
- baseline_vas
baseline shoulder pain on activity (VAS 0-100)
- pain_vas_activity
shoulder pain on activity at 24 months (VAS 0-100)
Source
Simulated (not real patient data), generated with the synthpop
package (sequential CART; Nowok, Raab and Dibben 2016,
doi:10.18637/jss.v074.i11
) from the FIMPACT 10-year trial
(BMJ 2025;391:e086201; dataset CC BY 4.0, University of Helsinki / Finnish
Ministry of Education open-data portal,
doi:10.23729/fd-d323a34b-f698-3bc6-b38d-9c93aeadbe74
), preserving its
covariate and covariate-outcome relationships; fidelity validated with the
syntheticdata package. See data-raw/simulate_external_data.R.
Details
The index and comparator arms come from the same trial, so both
carry the study label "FIMPACT". Splitting one randomized trial into a
single-arm IPD source and a single-arm aggregate source is what makes this
an unanchored example that still has a full-data comparison to be checked
against: fitting the two arms together on the complete synthetic records
gives the quantity the unanchored methods are trying to recover once one
arm has been reduced to summaries. That is a data-reduction benchmark. It
is not a randomized reference and not a known population causal effect.
The generator draws treatment first and then synthesizes the covariates
conditional on it, so the synthetic arms are not a fresh randomized
assignment independent of the generated baseline variables; and the
full-data estimate carries its own sampling error. Validating an estimator
against a causal target would need a specified covariate distribution,
potential-outcome mechanism, assignment rule and estimand, which these
records do not provide.
Because the label
is shared, combine_data warns that IPD and AgD come from
the same study; that warning is expected here and is the honest reading of
the data. It does not fire for psoriasis_ipd or
ndmm_ipd, whose arms really do come from different trials.