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REVIEW 1 major objections 8 references

Simple Survival End Point Calculator

T0 review · 1 major / 0 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read A visual web application computes sample sizes for survival endpoints and matches outputs from the lrstat and powerSurvEpi R packages.

desk verdict This is a web wrapper around two existing R packages for survival sample-size calculations, with no new methods or derivations. read the letter →

arxiv 2606.22435 v1 pith:5X27UCYM submitted 2026-06-21 stat.AP

Shaodong Wenren , Dingyan Cao , Zhiheng Jiang , Chenyu Li This is my paper · ORCID
classification stat.AP
keywords samplesizecalculationsurvivalendpointswebapplicationRpackageslrstatpowerSurvEpilogrankstatisticdropoutrate
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper develops a web-based tool to make sample size calculations for survival studies easier to use. Survival endpoint planning must often include dropout rates and delayed onset, which the underlying R packages already handle through extensions of the logrank method. The application provides a visual interface that calls these packages and returns the same numerical results as direct package calls on the tested cases. This matters for researchers who need accurate power calculations to design studies that can detect treatment effects without excess participants. The tests confirm the wrapper reproduces the package behavior without visible discrepancies.

What carries the argument

The visual web application that serves as a wrapper around the lrstat and powerSurvEpi R packages to generate survival sample size estimates.

What would settle it

Running the same test cases through the web application and through direct calls to the lrstat and powerSurvEpi functions in R; any numerical mismatch in the resulting sample sizes would falsify the consistency claim.

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Extended reading notes

Core claim

The authors developed a visual web application on the basis of the R packages lrstat and powerSurvEpi that calculates sample sizes for survival endpoints while incorporating dropout rate and delayed onset. The application was tested against provided cases and produces results basically consistent with the packages, proving the effectiveness of the application.

Load-bearing premise

The R packages lrstat and powerSurvEpi correctly implement the sample size formulas including dropout and delayed onset, and the web wrapper faithfully reproduces their outputs without introducing bugs or interface errors.

Editorial extensions

If this is right

  • Users obtain sample size estimates that incorporate dropout and delayed onset without writing R code.
  • The estimates follow the logrank-based methods and their extensions already implemented in the source packages.
  • The interface makes the calculations available through a browser rather than a command-line session.
  • Verification against the original packages confirms the tool can be used for the same planning tasks the packages support.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Making the application public could allow non-programmers to apply the same formulas that statisticians currently run directly in R.
  • If the source packages receive updates for new survival methods, the web tool would need corresponding updates to maintain consistency.
  • The approach of wrapping existing packages could be repeated for other endpoint types if similar R implementations exist.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The manuscript describes the development of a visual web application for calculating sample sizes for survival endpoints. It is based on the R packages lrstat and powerSurvEpi, incorporates factors such as dropout rates and delayed onset, and claims that testing demonstrates basic consistency with provided cases, thereby proving the application's effectiveness.

Significance. If the central claim holds, the application would offer a user-friendly web interface for complex survival sample size calculations that account for dropout and delayed entry, making these methods more accessible to researchers without requiring direct use of R packages. The contribution is primarily in software usability rather than new statistical methodology or derivations.

major comments (1)
  1. [Abstract] The assertion that 'the test results are basically consistent with the case' lacks any quantitative error metrics, detailed description of the test cases used, or provision of the application/source code for independent verification. This absence prevents assessment of whether the web wrapper faithfully reproduces the package outputs without introducing bugs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback regarding the validation details in our manuscript on the Simple Survival End Point Calculator web application. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] The assertion that 'the test results are basically consistent with the case' lacks any quantitative error metrics, detailed description of the test cases used, or provision of the application/source code for independent verification. This absence prevents assessment of whether the web wrapper faithfully reproduces the package outputs without introducing bugs.

    Authors: We agree that the manuscript's description of the testing is insufficient to allow independent verification of the web application's fidelity to the underlying R packages. In the revised version, we will expand both the abstract and the main text to include: (1) a detailed list of the test cases with all input parameters, (2) quantitative error metrics (e.g., absolute and relative differences between web-app outputs and direct calls to lrstat/powerSurvEpi), and (3) a public link to the source code repository so that readers can reproduce the checks. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity identified

full rationale

The manuscript describes development of a web application wrapper around the pre-existing lrstat and powerSurvEpi R packages for survival sample-size calculations. No new statistical derivations, formulas, or parameter fits are presented; the central claim is limited to faithful reproduction of the packages' outputs on supplied test cases. Because the work introduces no internal equations or self-referential predictions, there are no load-bearing steps that reduce to the paper's own inputs by construction.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

The central claim rests on the correctness of two external R packages whose source and validation are not re-examined here; no free parameters, new axioms, or invented entities are introduced by the paper itself.

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Cite this review

Pith. "Pith review of Simple Survival End Point Calculator." pith.science (2026). https://pith.science/paper/5X27UCYM

@misc{pith2026260622435,
  author       = {Pith},
  title        = {Pith review of: Simple Survival End Point Calculator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5X27UCYM}},
  note         = {Machine review of arXiv:2606.22435}
}
read the original abstract

In the study of survival endpoints, the size of the sample is crucial. The calculation of the sample size for survival endpoints was first proposed and presented by Freedman based on the logrank statistic. Later, many statisticians gradually introduced multiple factors such as dropout rate and delayed onset, proposed various estimation methods including the lrstat method, and developed corresponding statistical programs. However, the application of professional statistical functions is relatively complex. Based on this, this paper has developed a corresponding visual web application on the basis of the R package lrstat and powerSurvEpi, and tested its functions and program accordingly. The test results are basically consistent with the case, proving the effectiveness of the application.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

8 extracted references · 2 canonical work pages

  1. [1]

    Tables of the number of patients required in clinical trials using the logrank test

    L Freedman. Tables of the number of patients required in clinical trials using the logrank test. Statistics in medicine, 1: 0 121--9, 01 1986. doi:10.1002/sim.4780050112

    work page doi:10.1002/sim.4780050112 1986

  2. [2]

    Sample size calculation for logrank test and prediction of number of events over time

    Kaifeng Lu. Sample size calculation for logrank test and prediction of number of events over time. Pharmaceutical statistics, 20, 09 2020. doi:10.1002/pst.2069

    work page doi:10.1002/pst.2069 2020

  3. [3]

    lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond, 2026

    Kaifeng Lu. lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond, 2026. URL https://CRAN.R-project.org/package=lrstat. R package version 0.3.2

    2026

  4. [4]

    powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies, 2025

    Weiliang Qiu, Jorge Chavarro, Ross Lazarus, Bernard Rosner, and Jing Ma. powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies, 2025. URL https://CRAN.R-project.org/package=powerSurvEpi. R package version 0.1.5

    2025

  5. [5]

    Tables of the number of patients required in clinical trials using the logrank test , volume =

    Freedman, L , year =. Tables of the number of patients required in clinical trials using the logrank test , volume =. Statistics in medicine , doi =

  6. [6]

    Sample size calculation for logrank test and prediction of number of events over time , volume =

    Lu, Kaifeng , year =. Sample size calculation for logrank test and prediction of number of events over time , volume =. Pharmaceutical statistics , doi =

  7. [7]

    2026 , note =

    lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond , author =. 2026 , note =

    2026

  8. [8]

    2025 , note =

    powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies , author =. 2025 , note =

    2025

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Reviewed June 26, 2026 · model on record in the stance chip above.