<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>gosukehommaex.r-universe.dev</title><link>https://gosukehommaex.r-universe.dev</link><description>Recent package updates in gosukehommaex</description><generator>R-universe</generator><image><url>https://github.com/gosukehommaex.png</url><title>R packages by gosukehommaex</title><link>https://gosukehommaex.r-universe.dev</link></image><lastBuildDate>Sat, 06 Jun 2026 07:25:58 GMT</lastBuildDate><item><title>[gosukehommaex] FastSurvival 0.2.0</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Provides fast alternatives to standard survival analysis
functions in the 'survival' package, together with tools for
time-to-event trial simulation and sequential analysis. The
estimation and testing functions cover a single-time-point
Kaplan-Meier estimator (survfit_fast()), log-rank tests
including weighted and stratified variants (survdiff_fast()), a
closed-form hazard ratio estimator based on the Pike-Halley
Estimator method (coxph_fast()), restricted mean survival time
(rmst_fast()), milestone survival comparison
(milestone_fast()), the max-combo test (maxcombo_fast()), the
robust modestly-weighted log-rank test (rmw_fast()), the
average hazard with survival weight (ahsw_fast()), and the
Kalbfleisch-Prentice average hazard ratio (ahr_fast()). The
simulation layer generates individual patient data
(simdata_fast()), performs interim or sequential analyses
(analysis_fast()), and aggregates operating characteristics
(simsummary_fast()). All functions are designed for repeated
evaluation inside large simulation loops, such as adaptive
sample-size re-estimation, probability-of-success calculations,
and regional consistency evaluation in multi-regional trials.
Core computations are implemented in 'C++' via 'Rcpp' for
maximum performance. Methodological background is described in
Collett (2014, ISBN:9780429196294).</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/27056557154</link><pubDate>Sat, 06 Jun 2026 07:25:58 GMT</pubDate><r:package>FastSurvival</r:package><r:version>0.2.0</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/fastsurvival</r:upstream><r:article><r:source>group-sequential-design.Rmd</r:source><r:filename>group-sequential-design.html</r:filename><r:title>Group sequential design with the simulation trio</r:title><r:created>2026-06-02 15:28:03</r:created><r:modified>2026-06-05 04:39:24</r:modified></r:article><r:article><r:source>FastSurvival.Rmd</r:source><r:filename>FastSurvival.html</r:filename><r:title>Introduction to FastSurvival</r:title><r:created>2026-05-12 07:28:53</r:created><r:modified>2026-06-04 11:48:28</r:modified></r:article><r:article><r:source>speed-comparison.Rmd</r:source><r:filename>speed-comparison.html</r:filename><r:title>Speed comparison</r:title><r:created>2026-06-02 15:28:03</r:created><r:modified>2026-06-05 04:39:24</r:modified></r:article><r:article><r:source>validation.Rmd</r:source><r:filename>validation.html</r:filename><r:title>Validation of FastSurvival</r:title><r:created>2026-06-02 15:28:03</r:created><r:modified>2026-06-05 04:39:24</r:modified></r:article></item><item><title>[gosukehommaex] BayesianQDM 0.1.0</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Provides comprehensive methods to calculate posterior
probabilities, posterior predictive probabilities, and
Go/NoGo/Gray decision probabilities for quantitative
decision-making under a Bayesian paradigm in clinical trials.
The package supports both single and two-endpoint analyses for
binary and continuous outcomes, with controlled, uncontrolled,
and external designs. For single continuous endpoints, three
calculation methods are available: numerical integration (NI),
Monte Carlo simulation (MC), and Moment-Matching approximation
(MM). For two continuous endpoints, a bivariate
Normal-Inverse-Wishart conjugate model is implemented with MC
and MM methods. For two binary endpoints, a
Dirichlet-multinomial model is implemented. External designs
incorporate historical data through power priors using exact
conjugate representations (Normal-Inverse-Chi-squared for
single continuous, Normal-Inverse-Wishart for two continuous,
and Dirichlet for binary endpoints), enabling closed-form
posterior computation without Markov chain Monte Carlo (MCMC)
sampling. This approach significantly reduces computational
burden while preserving complete Bayesian rigor. The package
also provides grid-search functions to find optimal Go and NoGo
thresholds that satisfy user-specified operating characteristic
criteria for all supported endpoint types and study designs. S3
print() and plot() methods are provided for all decision
probability classes, enabling formatted display and
visualisation of Go/NoGo/Gray operating characteristics across
treatment scenarios. See Kang, Yamaguchi, and Han (2026)
&lt;doi:10.1080/10543406.2026.2655410&gt; for the methodological
framework.</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/26275310327</link><pubDate>Wed, 22 Apr 2026 02:34:37 GMT</pubDate><r:package>BayesianQDM</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/bayesianqdm</r:upstream><r:article><r:source>overview.Rmd</r:source><r:filename>overview.html</r:filename><r:title>Overview of BayesianQDM</r:title><r:created>2026-02-19 14:26:24</r:created><r:modified>2026-03-31 03:31:24</r:modified></r:article><r:article><r:source>single-binary.Rmd</r:source><r:filename>single-binary.html</r:filename><r:title>Single Binary Endpoint</r:title><r:created>2026-02-25 14:55:19</r:created><r:modified>2026-03-09 12:57:02</r:modified></r:article><r:article><r:source>single-continuous.Rmd</r:source><r:filename>single-continuous.html</r:filename><r:title>Single Continuous Endpoint</r:title><r:created>2026-02-25 06:40:50</r:created><r:modified>2026-03-31 03:31:24</r:modified></r:article><r:article><r:source>two-binary.Rmd</r:source><r:filename>two-binary.html</r:filename><r:title>Two Binary Endpoints</r:title><r:created>2026-02-25 14:55:19</r:created><r:modified>2026-03-09 12:57:02</r:modified></r:article><r:article><r:source>two-continuous.Rmd</r:source><r:filename>two-continuous.html</r:filename><r:title>Two Continuous Endpoints</r:title><r:created>2026-02-25 14:55:19</r:created><r:modified>2026-03-31 04:50:16</r:modified></r:article></item><item><title>[gosukehommaex] SingleArmMRCT 0.1.1</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Provides functions to calculate and visualise the Regional
Consistency Probability (RCP) for single-arm multi-regional
clinical trials (MRCTs) using the Effect Retention Approach
(ERA). Six endpoint types are supported: continuous, binary,
count (negative binomial), time-to-event via hazard ratio,
milestone survival, and restricted mean survival time (RMST).
For each endpoint, both a closed-form (or semi-analytical)
solution and a Monte Carlo simulation approach are implemented.
Two consistency evaluation methods are available: Method 1
(effect retention in Region 1 relative to the overall
population) and Method 2 (simultaneous positive effect across
all regions). Plotting functions generate faceted
visualisations of RCP as a function of the regional allocation
proportion, overlaying formula and simulation results for
direct comparison. The methodology follows the Japanese MHLW
guidelines for MRCTs. Abbreviations used: RCP (Regional
Consistency Probability), MRCT (Multi-Regional Clinical Trial),
RMST (Restricted Mean Survival Time), MHLW (Ministry of Health,
Labour and Welfare).</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/25547597724</link><pubDate>Thu, 02 Apr 2026 00:44:48 GMT</pubDate><r:package>SingleArmMRCT</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/singlearmmrct</r:upstream><r:article><r:source>introduction.Rmd</r:source><r:filename>introduction.html</r:filename><r:title>Introduction to SingleArmMRCT</r:title><r:created>2026-03-28 13:53:53</r:created><r:modified>2026-03-29 04:24:54</r:modified></r:article><r:article><r:source>non-survival-endpoints.Rmd</r:source><r:filename>non-survival-endpoints.html</r:filename><r:title>Non-Survival Endpoints: Continuous, Binary, and Count</r:title><r:created>2026-03-28 13:53:53</r:created><r:modified>2026-03-29 04:24:54</r:modified></r:article><r:article><r:source>survival-endpoints.Rmd</r:source><r:filename>survival-endpoints.html</r:filename><r:title>Survival Endpoints: Hazard Ratio, Milestone Survival, and RMST</r:title><r:created>2026-03-28 13:53:53</r:created><r:modified>2026-03-29 04:24:54</r:modified></r:article></item><item><title>[gosukehommaex] twoCoprimary 1.0.0</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Comprehensive functions to calculate sample size and power
for clinical trials with two co-primary endpoints. The package
supports five endpoint combinations: two continuous endpoints
(Sozu et al. 2011 &lt;doi:10.1080/10543406.2011.551329&gt;), two
binary endpoints using asymptotic methods (Sozu et al. 2010
&lt;doi:10.1002/sim.3972&gt;) and exact methods (Homma and Yoshida
2025 &lt;doi:10.1177/09622802251368697&gt;), mixed continuous and
binary endpoints (Sozu et al. 2012
&lt;doi:10.1002/bimj.201100221&gt;), and mixed count and continuous
endpoints (Homma and Yoshida 2024 &lt;doi:10.1002/pst.2337&gt;). All
methods appropriately account for correlation between endpoints
and provide both sample size and power calculation
capabilities.</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/26707338005</link><pubDate>Thu, 01 Jan 2026 07:18:01 GMT</pubDate><r:package>twoCoprimary</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/twocoprimary</r:upstream><r:article><r:source>mixed-continuous-binary.Rmd</r:source><r:filename>mixed-continuous-binary.html</r:filename><r:title>Mixed Continuous and Binary Co-Primary Endpoints</r:title><r:created>2025-11-02 08:44:09</r:created><r:modified>2025-11-09 13:33:52</r:modified></r:article><r:article><r:source>mixed-count-continuous.Rmd</r:source><r:filename>mixed-count-continuous.html</r:filename><r:title>Mixed Count and Continuous Co-Primary Endpoints</r:title><r:created>2025-11-02 08:44:09</r:created><r:modified>2025-11-09 15:22:24</r:modified></r:article><r:article><r:source>overview.Rmd</r:source><r:filename>overview.html</r:filename><r:title>Overview of Two Co-Primary Endpoints Analysis</r:title><r:created>2025-11-02 13:50:02</r:created><r:modified>2025-11-09 15:22:24</r:modified></r:article><r:article><r:source>two-binary-endpoints-approx.Rmd</r:source><r:filename>two-binary-endpoints-approx.html</r:filename><r:title>Two Binary Co-Primary Endpoints (Asymptotic Methods)</r:title><r:created>2025-11-02 08:44:09</r:created><r:modified>2025-11-09 15:22:24</r:modified></r:article><r:article><r:source>two-binary-endpoints-exact.Rmd</r:source><r:filename>two-binary-endpoints-exact.html</r:filename><r:title>Two Binary Co-Primary Endpoints (Exact Methods)</r:title><r:created>2025-11-02 08:44:09</r:created><r:modified>2025-11-09 13:33:52</r:modified></r:article><r:article><r:source>two-continuous-endpoints.Rmd</r:source><r:filename>two-continuous-endpoints.html</r:filename><r:title>Two Continuous Co-Primary Endpoints</r:title><r:created>2025-11-02 08:44:09</r:created><r:modified>2025-11-09 15:22:24</r:modified></r:article></item><item><title>[gosukehommaex] simFastBOIN 1.3.2</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Conducting Bayesian Optimal Interval (BOIN) design for
phase I dose-finding trials. 'simFastBOIN' provides functions
for pre-computing decision tables, conducting trial
simulations, and evaluating operating characteristics. The
package uses vectorized operations and the Iso::pava() function
for isotonic regression to achieve efficient performance while
maintaining full compatibility with BOIN methodology. Version
1.3.2 adds p_saf and p_tox parameters for customizable safety
and toxicity thresholds. Version 1.3.1 fixes Date field.
Version 1.2.1 adds comprehensive 'roxygen2' documentation and
enhanced print formatting with flexible table output options.
Version 1.2.0 integrated C-based PAVA for isotonic regression.
Version 1.1.0 introduced conservative MTD selection (boundMTD)
and flexible early stopping rules (n_earlystop_rule). Methods
are described in Liu and Yuan (2015) &lt;doi:10.1111/rssc.12089&gt;.</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/25984241412</link><pubDate>Thu, 11 Dec 2025 14:07:43 GMT</pubDate><r:package>simFastBOIN</r:package><r:version>1.3.2</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/simfastboin</r:upstream><r:article><r:source>simFastBOIN-introduction.Rmd</r:source><r:filename>simFastBOIN-introduction.html</r:filename><r:title>Introduction to simFastBOIN</r:title><r:created>2025-12-05 15:40:51</r:created><r:modified>2025-12-05 15:56:04</r:modified></r:article></item><item><title>[gosukehommaex] bbssr 1.0.2</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Provides comprehensive tools for blinded sample size
re-estimation (BSSR) in two-arm clinical trials with binary
endpoints. Unlike traditional fixed-sample designs, BSSR allows
adaptive sample size adjustments during trials while
maintaining statistical integrity and study blinding.
Implements five exact statistical tests: Pearson chi-squared,
Fisher exact, Fisher mid-p, Z-pooled exact unconditional, and
Boschloo exact unconditional tests. Supports restricted,
unrestricted, and weighted BSSR approaches with exact Type I
error control. Statistical methods based on Mehrotra et al.
(2003) &lt;doi:10.1111/1541-0420.00051&gt; and Kieser (2020)
&lt;doi:10.1007/978-3-030-49528-2_21&gt;.</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/26084653383</link><pubDate>Wed, 18 Jun 2025 21:37:38 GMT</pubDate><r:package>bbssr</r:package><r:version>1.0.2</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/bbssr</r:upstream><r:article><r:source>bbssr-introduction.Rmd</r:source><r:filename>bbssr-introduction.html</r:filename><r:title>Introduction to bbssr: Blinded Sample Size Re-estimation for Binary Endpoints</r:title><r:created>2025-06-13 16:10:35</r:created><r:modified>2025-06-14 13:22:11</r:modified></r:article><r:article><r:source>bbssr-statistical-methods.Rmd</r:source><r:filename>bbssr-statistical-methods.html</r:filename><r:title>Statistical Methods in bbssr</r:title><r:created>2025-06-13 16:10:35</r:created><r:modified>2025-06-14 13:22:11</r:modified></r:article><r:article><r:source>bbssr-validation.Rmd</r:source><r:filename>bbssr-validation.html</r:filename><r:title>Validation of bbssr Package Functions</r:title><r:created>2025-06-14 05:23:50</r:created><r:modified>2025-06-17 16:08:13</r:modified></r:article></item><item><title>[gosukehommaex] RegionalConsistency 1.0.0</title><author>my.name.is.gosuke@gmail.com (Gosuke Homma)</author><description>Provides methods to calculate approximate regional
consistency probabilities using Method 1 and Method 2 proposed
by the Japanese Ministry of Health, Labor and Welfare (2007)
&lt;https://www.pmda.go.jp/files/000153265.pdf&gt;. These methods are
useful for assessing regional consistency in multi-regional
clinical trials.  The package can calculate unconditional,
joint, and conditional regional consistency probabilities.  For
technical details, please see Homma (2024)
&lt;doi:10.1002/pst.2358&gt;.</description><link>https://github.com/r-universe/gosukehommaex/actions/runs/25781687137</link><pubDate>Wed, 28 May 2025 14:43:36 GMT</pubDate><r:package>RegionalConsistency</r:package><r:version>1.0.0</r:version><r:status>success</r:status><r:repository>https://gosukehommaex.r-universe.dev</r:repository><r:upstream>https://github.com/gosukehommaex/regionalconsistency</r:upstream></item></channel></rss>