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Comparison · Analytics

RBesT vs simtrial

A side-by-side editorial comparison of RBesT and simtrial — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:clinical-trials

RBesT vs simtrial: at a glance

FeatureRBesTsimtrial
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesbayesian-statistics, clinical-trials, stan, r-languageclinical-trials, group-sequential, survival-analysis, simulation
Last editorial update1h ago49m ago
WebsiteVisit →Visit →

What is RBesT?

RBesT is teaching its Bayesian decision rules to answer two-sided questions.

RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.

Read the full RBesT trajectory →

What is simtrial?

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.

Read the full simtrial trajectory →

RBesT vs simtrial: editorial side-by-side

R
RBesT
ANALYTICS
0.0

RBesT is teaching its Bayesian decision rules to answer two-sided questions.

◆ Current state

RBesT builds meta-analytic-predictive priors — the machinery for borrowing historical control data into a new trial — and evaluates the operating characteristics of decisions made with them. The stable line has spent several releases on effective sample size: ESS for normal mixtures via a new `family` argument, boundary corrections when no responses or no non-responses are observed, and stabilised ELIR computations. The 1.9-0 release candidate extends the normal, binomial and Poisson outcome functions to two-sided decisions.

◆ Where it's heading

Two currents run through the changelog. One is ESS hardening — nearly every release since 1.7-4 fixes another edge case where the ELIR calculation aborted or returned something unstable, which is what happens when a quantity used to justify prior strength to regulators gets scrutinised. The other is Stan and brms integration debt: array syntax updates, a minimum Stan version bump, truncated prior generation for `mixstanvar`, deterministic EM. The RC's contributor list shows a second active maintainer, and the work is broader than any recent stable release.

◆ Prediction

The release candidate covers all three outcome families and has already absorbed a round of review comments, so the next step is most likely the 1.9-0 CRAN release itself rather than further feature work.

S
simtrial
ANALYTICS
0.0

A fixed-design trial simulator grew a pluggable test framework, then spent a year proving the numbers

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

Alternatives to RBesT and simtrial

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 RBesT or simtrial.

See all RBesT alternatives → · See all simtrial alternatives →

Recent activity from RBesT and simtrial

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 5mo agoRBesTTwo-sided decisions across normal, binomial and Poisson outcomes
  2. 8mo agosimtrialOne-sided efficacy bound and stratified cut date corrected
  3. 11mo agosimtrialsim_gs_n moved to data.table; stratified design example added
  4. 1y agosimtrial1.0.0 settles the wlr interface and documents both simulation paths
  5. 1y agoRBesTJSON read and write for mixture objects
  6. 1y agoRBesTess() fixed inside apply functions
  7. 1y agoRBesTESS for normal mixtures in the exponential family
  8. 1y agosimtrialMilestone Z-score denominator corrected; parallel sim_fixed_n arrives
  9. 1y agoRBesTTruncated mixture priors for brms, plus faster Stan models
  10. 2y agosimtrialChecks pass without Suggests dependencies
  11. 2y agosimtrialRMST and milestone tests, plus a user-definable cut and test framework
  12. 2y agoRBesTStan array syntax update and CRAN system requirements

Frequently asked questions

What is the difference between RBesT and simtrial?

Both compete on the same themes — clinical-trials — within Analytics. RBesT and simtrial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is RBesT better than simtrial?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. RBesT and simtrial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to RBesT?

Top RBesT alternatives in Analytics are ranked by recent ship velocity. Browse the "RBesT alternatives" section above for the current picks, or visit /alternatives/rbest for the full list with editorial commentary on each.

What are the best alternatives to simtrial?

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.