Per-comparison change in pain on a 0-10 cm visual analogue scale (VAS) for
an exercise-therapy arm versus a control arm: the 20 comparisons, from 18
randomized trials, pooled for the VAS outcome by Li, Bao, Wang and Zhao
(2025). The change scores
are post minus baseline VAS, so a more negative value is a larger pain
reduction; analyze with direction = "lower" and a negative MID equal to the
required reduction (for example mid = -1.5 for a 1.5 cm responder threshold).
Format
A data frame with 20 rows and 8 columns:
- study
Comparison label (first author and year).
- change_e
Mean VAS change in the exercise (experimental) arm.
- sd_e
Standard deviation of the change in the exercise arm.
- n_e
Exercise-arm sample size.
- change_c
Mean VAS change in the control arm.
- sd_c
Standard deviation of the change in the control arm.
- n_c
Control-arm sample size.
- trial
Trial label; equal for the comparisons of one multi-arm trial.
Source
Li Z, Bao Z, Wang S, Zhao M (2025). Meta-analysis of the best exercise mode and dose study for improving spinal health. Frontiers in Sports and Active Living, 7, 1614906. doi:10.3389/fspor.2025.1614906 . Figure 3 (VAS pain). Reproduced under the Creative Commons Attribution License (CC BY 4.0); the original authors and journal are credited as required.
Details
Two trials had three arms and contribute two exercise-versus-control
comparisons each, reusing one control group: Farzaneh 2022 (rows
"Farzaneh a" and "Farzaneh b") and Peng Qiu 2014 ("Peng Qiu a" and
"Peng Qiu b"). The trial column marks them, and respondeR divides each
shared control arm among its comparisons (Cochrane Handbook, section 23.3.4)
so those patients are not counted twice. The values are as extracted by the
source meta-analysis; its figure lists 14 patients in both Farzaneh exercise
arms, whereas the trial report gives 12 for one of them. The published
extraction is kept so the dataset reproduces the source forest plot.
Examples
# Proportion achieving at least a 1.5 cm VAS reduction (responder),
# exercise versus control, random effects with the small-k HKSJ interval.
responder_analysis(vas_pain, mid = -1.5, direction = "lower",
pooling = "random", ci_method = "hksj_adhoc")
#> Responder analysis (respondeR)
#> MID = -1.5 (a lower change is a response); k = 20 studies; random
#> effects; delta per-study variance
#> Percentages; 95% confidence intervals.
#>
#> Method PE PC RD RR
#> Individual - - 17.5 (10.8 to 24.3) 1.20 (1.09 to 1.33)
#> Weighted mean 79.2 52.3 26.9 (18.9 to 34.8) 1.51 (1.25 to 1.83)
#> Unweighted mean 78.7 47.1 31.6 1.67
#> Median 83.9 37.3 46.7 2.25
#> OR NNT
#> 3.90 (2.54 to 5.99) NNTB 5.7 (4.1 to 9.3)
#> 3.47 (2.44 to 4.92) NNTB 3.7 (2.9 to 5.3)
#> 4.14 NNTB 3.2
#> 8.81 NNTB 2.1
#>
#> individual: I-squared 62%, tau-squared 0.00712, Q 49.67 (p 0.000); prediction interval -1.5 to 36.5
#>
#> Use as.data.frame() for every column, summary() for details, plot() for a forest plot.