r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
A side-by-side editorial comparison of rbmi and simtrial — release velocity, themes, recent moves, and the top alternatives to consider.
Reference-based multiple imputation for trials, now shipping without Bayesian support by default.
rbmi implements reference-based multiple imputation for longitudinal clinical trial data with missing values — the estimand machinery regulators expect for handling intercurrent events and dropout. The consequential recent change was 1.3.0 moving rstan from a hard dependency to Suggests, which takes Bayesian imputation out of the default install. Since then the work has been documentation and nomenclature discipline: 1.6.1 standardized on MNAR over a mixed NMAR/MNAR vocabulary and deprecated the nmar.rm argument accordingly.
A fixed-design trial simulator grew a pluggable test framework, then spent a year proving the numbers
simtrial simulates time-to-event clinical trials and applies the tests used to analyse them — logrank, weighted logrank, MaxCombo, RMST, milestone. The 0.4.0 release turned it from a fixed-sample simulator into a group sequential one and standardised every test behind a common output contract, and the releases since have been about making that machinery correct and fast enough to run at scale. Version 1.0.0 arrived in June 2025 with the API settled and three vignettes explaining both the one-call and build-it-yourself paths.
rbmi implements reference-based multiple imputation for longitudinal clinical trial data with missing values — the estimand machinery regulators expect for handling intercurrent events and dropout. The consequential recent change was 1.3.0 moving rstan from a hard dependency to Suggests, which takes Bayesian imputation out of the default install. Since then the work has been documentation and nomenclature discipline: 1.6.1 standardized on MNAR over a mixed NMAR/MNAR vocabulary and deprecated the nmar.rm argument accordingly.
The package is optimizing for adoption friction over feature breadth. Dropping a compiled Stan dependency from the default install, deprecating a bespoke seed argument in favor of base set.seed(), and aligning lsmeans() behavior and weight naming with emmeans all point the same direction — behave like a conventional R package rather than a specialized one. Documentation work in 1.6.1 covering @return on every exported function and executable examples reads as preparation for validation scrutiny rather than user demand.
Given the FAQ vignette's validation statement and the recent documentation completeness pass, the next work is more likely qualification and estimand documentation than new imputation methods.
simtrial simulates time-to-event clinical trials and applies the tests used to analyse them — logrank, weighted logrank, MaxCombo, RMST, milestone. The 0.4.0 release turned it from a fixed-sample simulator into a group sequential one and standardised every test behind a common output contract, and the releases since have been about making that machinery correct and fast enough to run at scale. Version 1.0.0 arrived in June 2025 with the API settled and three vignettes explaining both the one-call and build-it-yourself paths.
Post-1.0 the work is almost entirely statistical correctness and speed, and it is concentrated in sim_gs_n(): one-sided efficacy bounds, stratified targeted-event cut dates, a helper that derives cuttings straight from the design object. Performance moves in one direction throughout — dplyr replaced by data.table, foreach combination replaced by manual assembly, parallelisation added to sim_fixed_n() — because simulation-based operating characteristics are only useful if you can afford enough replications.
The recent fixes cluster on stratified and group sequential paths, so the next release most likely continues there rather than adding a new test type. The cut_from_design() helper suggests tighter coupling to gsDesign2 design objects is the direction of travel.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either rbmi or simtrial.
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul took mocking back from webmockr and made it a property of the client itself
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all rbmi alternatives → · See all simtrial alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — clinical-trials, pharmaverse — within Analytics. rbmi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rbmi is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top rbmi alternatives in Analytics are ranked by recent ship velocity. Browse the "rbmi alternatives" section above for the current picks, or visit /alternatives/rbmi for the full list with editorial commentary on each.
Top simtrial alternatives in Analytics are ranked by recent ship velocity. Browse the "simtrial alternatives" section above for the current picks, or visit /alternatives/simtrial for the full list with editorial commentary on each.