The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

respondeR 0.1.0

First public release. The package began life as a small Shiny app; this release rebuilds it as a documented, tested R package with the statistics in pure, exported functions and the Shiny app as a thin front end.

Robustness and methodology hardening

In response to an external methodology audit:

• Boundary handling. Extreme MIDs that push a responder probability to exactly 0 or 1 no longer crash or return infinite intervals; probabilities are clamped away from 0 and 1 only where they feed logs, logits and weights, while the reported proportions and risk difference stay unclamped.
• Small-study random effects. Added ci_method = "hksj" for the Hartung-Knapp-Sidik-Jonkman interval, better calibrated than Wald when the number of studies is small.
• Weighted-method variance now propagates uncertainty in the pooled SD as well as the pooled mean (full delta method), so its intervals are no longer too narrow. Weighted confidence intervals are slightly wider than in the first build; the point estimates are unchanged.
• Stricter validation. Sample sizes must be whole numbers; the lognormal model now rejects negative study-arm mean changes before aggregation; and the vector building blocks reject non-finite sd/n.
• The binomial SE is documented as a pseudo-binomial approximation, with delta recommended for summary-statistic inputs.

Features

• responder_analysis(): the main entry point. Converts continuous arm summaries (mean change, SD, n) into responder proportions and a tidy table of between-arm effect measures: risk difference, risk ratio, odds ratio and number needed to treat. Pooling methods: "individual", "weighted", "unweighted", "median" and "smd".
• responder_rd_individual() and responder_proportions(): exported building blocks for per-study risk differences and arm responder probabilities.
• responder_cles(): threshold-free common-language effect size (the probabilistic index), pooled fixed or random effects, requiring no MID.
• format_responder_results(): display-ready formatting for reports and the app.
• launch_responder_analysis(): launches the bundled ResponderAnalysis Shiny application (direction toggle, method/pooling/interval options, RR/OR/NNT, a per-study forest plot, CLES and CSV I/O).
• sample_responder_data: small bundled example dataset.
• vas_pain: a real bundled dataset of 20 exercise-for-spinal-health trials pooled on the VAS pain scale (Li et al., 2025, doi:10.3389/fspor.2025.1614906, reproduced under CC BY 4.0).

Methods and options

• Random effects. The individual and SMD methods support fixed- or random-effects pooling (pooling = "random") with DerSimonian-Laird (dependency-free) or REML (tau_method = "REML", via metafor) between-study variance, reporting Cochran’s Q, I-squared, tau-squared and a prediction interval.
• Relative measures. Risk ratio and odds ratio (log-scale pooling) and number needed to treat (Altman, 1998) accompany the absolute risk difference.
• SMD bridge. The "smd" method pools the standardized mean difference (Hedges’ g) and maps it to an odds ratio through the logistic link, combined with the weighted-pooled control risk (Cox; Chinn, 2000).
• Refinements. Logit-transformed proportion intervals and Newcombe MOVER risk-difference intervals (ci_type = "logit"); propagation of uncertainty in the MID threshold (mid_sd); alternative change-score distributions (dist = "lognormal" or "t").
• Baseline-risk option. control = "median" takes the control responder proportion from the median control arm for every summary method (the Sofi-Mahmudi 2024 simulation baseline), while still pooling the experimental arm by the chosen method; the default control = "matched" pools the control arm the same way as the experimental arm. Because the median control arm has no sampling-variance model, control = "median" returns point estimates only.

Statistical corrections (results differ from the previous app)

The earlier Shiny app contained several errors that are fixed here. Numbers from the weighted and unweighted methods, and all confidence intervals, will differ from that app; the individual and median point estimates are unchanged.

• The control responder proportion is, by default, computed with the same pooling as the experimental arm (previously every method reused the median method’s control proportion). The earlier median-baseline behavior remains available as an explicit, documented choice via control = "median".
• The weighted method now pools the SD with the within-study pooled SD sqrt(sum((n-1) sd^2) / sum(n-1)) rather than inverse-variance pooling of SDs, with a delta-method variance for the risk difference.
• The median and unweighted methods are reported as point-estimate summaries (no variance model) instead of carrying a spurious interval.
• Proportions are kept on the [0, 1] scale internally, removing a class of percent-scale variance errors.

Removed

• The unused and incorrect Mantel-Haenszel helpers rr_meta(), rd_meta() and prop_meta() (which operated on a 2x2 count format the package never produced; rr_meta() returned swapped confidence limits) have been removed, along with the duplicated, unvalidated iv_meta().

Infrastructure

• Full roxygen2 documentation with runnable examples and references.
• A testthat suite (104 checks) covering known-answer results, regression anchors, edge cases and every method.
• A pkgdown website, methodology and getting-started vignettes, and a hex logo.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.