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Comparison · Analytics

simtrial vs Tplyr

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

Shared themes:clinical-trialspharmaverse

simtrial vs Tplyr: at a glance

FeaturesimtrialTplyr
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesclinical-trials, group-sequential, survival-analysis, simulationclinical-trials, tables, traceability, r-package
Last editorial update1h ago57m ago
WebsiteVisit →Visit →

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 →

What is Tplyr?

Tplyr made clinical summary tables explain where every number came from.

Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.

Read the full Tplyr trajectory →

simtrial vs Tplyr: editorial side-by-side

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.

T
Tplyr
ANALYTICS
0.0

Tplyr made clinical summary tables explain where every number came from.

◆ Current state

Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.

◆ Where it's heading

Post-1.0 work has been about completing the metadata story and filling gaps in layer composition rather than adding table types — metadata for missing subjects, add_anti_join(), missing-subject rows, data limiting, and fixes to nested count layers where an inner value appears under several outer groups. Releases cluster tightly after a major version, then go quiet, and the window ends with a patch issued days after the release it corrects.

◆ Prediction

Further releases will most likely continue closing traceability and nested-layer edge cases rather than introducing new layer types, following the pattern of both post-1.0 feature releases.

Alternatives to simtrial and Tplyr

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 simtrial or Tplyr.

See all simtrial alternatives → · See all Tplyr alternatives →

Recent activity from simtrial and Tplyr

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

  1. 8mo agosimtrialOne-sided efficacy bound and stratified cut date corrected
  2. 11mo agosimtrialsim_gs_n moved to data.table; stratified design example added
  3. 1y agosimtrial1.0.0 settles the wlr interface and documents both simulation paths
  4. 1y agosimtrialMilestone Z-score denominator corrected; parallel sim_fixed_n arrives
  5. 2y agosimtrialChecks pass without Suggests dependencies
  6. 2y agosimtrialRMST and milestone tests, plus a user-definable cut and test framework
  7. 2y agoTplyrMissing-subject metadata, add_anti_join(), and nested-layer fixes
  8. 3y agoTplyrMetadata vignette fix and parenthesis hugging
  9. 3y agoTplyrDenominator logic fix ahead of CRAN release
  10. 3y agoTplyrReverse-dependency fix
  11. 3y agoTplyr1.0.0 introduces the traceability metadata framework

Frequently asked questions

What is the difference between simtrial and Tplyr?

Both compete on the same themes — clinical-trials, pharmaverse — within Analytics. simtrial and Tplyr 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 simtrial better than Tplyr?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. simtrial and Tplyr 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 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.

What are the best alternatives to Tplyr?

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