<?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>zhongheng-biostatistics.r-universe.dev</title><link>https://zhongheng-biostatistics.r-universe.dev</link><description>Recent package updates in zhongheng-biostatistics</description><generator>R-universe</generator><image><url>https://github.com/zhongheng-biostatistics.png</url><title>R packages by zhongheng-biostatistics</title><link>https://zhongheng-biostatistics.r-universe.dev</link></image><lastBuildDate>Fri, 10 Apr 2026 11:15:04 GMT</lastBuildDate><item><title>[zhongheng-biostatistics] WCRBayesDesign 1.0.1</title><author>zhonghengcai123@gmail.com (Zhongheng Cai)</author><description>Calibrates Bayesian two-stage designs for single-arm phase
II trials with time-to-event endpoints using a window-cohort
with controlled roll-on. Interim monitoring is anchored to a
locked interim cohort and a pre-specified follow-up
requirement, so analysis timing remains predictable while
preserving follow-up maturity. The package searches feasible
interim rules, optimizes final sample size and decision
thresholds, evaluates operating characteristics by Monte Carlo
simulation, and supports exponential, Weibull, log-normal,
log-logistic, and user-defined baseline survival models.
Related published foundations include Simon (1989)
&lt;doi:10.1016/0197-2456(89)90015-9&gt; and Cotterill and Whitehead
(2015) &lt;doi:10.1002/sim.6426&gt;.</description><link>https://github.com/r-universe/zhongheng-biostatistics/actions/runs/25656728700</link><pubDate>Fri, 10 Apr 2026 11:15:04 GMT</pubDate><r:package>WCRBayesDesign</r:package><r:version>1.0.1</r:version><r:status>success</r:status><r:repository>https://zhongheng-biostatistics.r-universe.dev</r:repository><r:upstream>https://github.com/cran/WCRBayesDesign</r:upstream></item><item><title>[zhongheng-biostatistics] DTEBOP2 1.0.3</title><author>zhonghengcai123@gmail.com (Zhongheng Cai)</author><description>Implements a Bayesian Optimal Phase II design (DTE-BOP2)
for trials with delayed treatment effects, particularly
relevant to immunotherapy studies where treatment benefits may
emerge after a delay. The method builds upon the BOP2 framework
and incorporates uncertainty in the delay timepoint through a
truncated gamma prior, informed by expert knowledge or default
settings. Supports two-arm trial designs with functionality for
sample size determination, interim and final analyses, and
comprehensive simulation under various delay and design
scenarios. Ensures rigorous type I and II error control while
improving trial efficiency and power when the delay effect is
present. A manuscript describing the methodology is under
development and will be formally referenced upon publication.</description><link>https://github.com/r-universe/zhongheng-biostatistics/actions/runs/27195408849</link><pubDate>Fri, 09 May 2025 10:40:06 GMT</pubDate><r:package>DTEBOP2</r:package><r:version>1.0.3</r:version><r:status>success</r:status><r:repository>https://zhongheng-biostatistics.r-universe.dev</r:repository><r:upstream>https://github.com/cran/DTEBOP2</r:upstream><r:article><r:source>DTEBOP2.Rmd</r:source><r:filename>DTEBOP2.html</r:filename><r:title>DTEBOP2:An R Package for Designing Two-Arm Multi-Stage Survival Trials with Delayed Treatment Effects</r:title><r:created>2025-05-09 10:40:06</r:created><r:modified>2025-05-09 10:40:06</r:modified></r:article></item></channel></rss>