{ "_id": "6a3c083ab2c3d1eea7a4d7e6", "Package": "gsDesign2", "Title": "Group Sequential Design with Non-Constant Effect", "Version": "1.1.8", "Authors@R": "c(\nperson(\"Keaven\", \"Anderson\", email = \"keaven_anderson@merck.com\", role = c(\"aut\")),\nperson(\"Yujie\", \"Zhao\", email = \"yujie.zhao@merck.com\", role = c(\"aut\", \"cre\")),\nperson(\"Yilong\", \"Zhang\", email = \"elong0527@gmail.com\", role = c(\"aut\")),\nperson(\"John\", \"Blischak\", email = \"jdblischak@gmail.com\", role = c(\"aut\")),\nperson(\"Yihui\", \"Xie\", email = \"yihui.xie@merck.com\", role = c(\"aut\")),\nperson(\"Nan\", \"Xiao\", email = \"nan.xiao1@merck.com\", role = c(\"aut\")),\nperson(\"Jianxiao\", \"Yang\", email = \"yangjx@ucla.edu\", role = c(\"aut\")),\nperson(\"Amin\", \"Shirazi\", email = \"ashirazist@gmail.com\", role = c(\"ctb\")),\nperson(\"Ruixue\", \"Wang\", email = \"ruixue.wang@merck.com\", role = c(\"ctb\")),\nperson(\"Yi\", \"Cui\", email = \"yi.cui@merck.com\", role = c(\"ctb\")),\nperson(\"Ping\", \"Yang\", email = \"ping.yang1@merck.com\", role = c(\"ctb\")),\nperson(\"Xin Tong\", \"Li\", email = \"xin.tong.li@merck.com\", role = c(\"ctb\")),\nperson(\"Chenxiang\", \"Li\", email = \"chenxiang.li@merck.com\", role = c(\"ctb\")),\nperson(\"Hiroaki\", \"Fukuda\", email = \"hiroaki.fukuda@merck.com\", role = c(\"ctb\")),\nperson(\"Hongtao\", \"Zhang\", email = \"hongtao.zhang1@merck.com\", role = c(\"ctb\")),\nperson(\"Yalin\", \"Zhu\", email = \"yalin.zhu@outlook.com\", role = c(\"ctb\")),\nperson(\"Shiyu\", \"Zhang\", email = \"shiyu.zhang@merck.com\", role = c(\"ctb\")),\nperson(\"Dickson\", \"Wanjau\", email = \"dickson.wanjau@merck.com\", role = c(\"ctb\")),\nperson(\"Merck & Co., Inc., Rahway, NJ, USA and its affiliates\", role = \"cph\",\ncomment = c(ROR = \"02891sr49\"))\n)", "Description": "The goal of 'gsDesign2' is to enable fixed or group\nsequential design under non-proportional hazards. To enable\nhighly flexible enrollment, time-to-event and time-to-dropout\nassumptions, 'gsDesign2' offers piecewise constant enrollment,\nfailure rates, and dropout rates for a stratified population.\nThis package includes three methods for designs: average hazard\nratio, weighted logrank tests in Yung and Liu (2019)\n, and MaxCombo tests. Substantial\nflexibility on top of what is in the 'gsDesign' package is\nintended for selecting boundaries.", "License": "GPL-3", "URL": "https://merck.github.io/gsDesign2/,\nhttps://github.com/Merck/gsDesign2", "BugReports": "https://github.com/Merck/gsDesign2/issues", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libxml2-dev\nlibssl-dev libnode-dev", "Repository": "https://merck.r-universe.dev", "Date/Publication": "2026-06-24 15:03:57 UTC", "RemoteUrl": "https://github.com/merck/gsdesign2", "RemoteRef": "HEAD", "RemoteSha": "68bc1ff178f6c54a23a5846f276ce52a2eac7d9b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-24 16:32:10 UTC", "User": "root" }, "Author": "Keaven Anderson [aut],\nYujie Zhao [aut, cre],\nYilong Zhang [aut],\nJohn Blischak [aut],\nYihui Xie [aut],\nNan Xiao [aut],\nJianxiao Yang [aut],\nAmin Shirazi [ctb],\nRuixue Wang [ctb],\nYi Cui [ctb],\nPing Yang [ctb],\nXin Tong Li [ctb],\nChenxiang Li [ctb],\nHiroaki Fukuda [ctb],\nHongtao Zhang [ctb],\nYalin Zhu [ctb],\nShiyu Zhang [ctb],\nDickson Wanjau [ctb],\nMerck & Co., Inc., Rahway, NJ, USA and its affiliates [cph] (ROR:\n)", "Maintainer": "Yujie Zhao ", "MD5sum": "635387e42fcf8cd1188eb29c40a42419", "_user": "merck", "_type": "src", "_file": "gsDesign2_1.1.8.tar.gz", "_fileid": "d4c448bb217fac91cafa8f79fdc921889ca0bde8d21f795ee916032bae650a9b", "_filesize": 856938, "_sha256": "d4c448bb217fac91cafa8f79fdc921889ca0bde8d21f795ee916032bae650a9b", "_created": "2026-06-24T16:32:10.000Z", "_published": "2026-06-24T16:39:22.764Z", "_distro": "resolute", "_jobs": [ { "job": 83248031963, "time": 264, "config": "linux-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "7856082764" }, { "job": 83248031942, "time": 254, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7856078159" }, { "job": 83248031980, "time": 264, "config": "linux-release-arm64", "r": "4.6.0", "check": "ERROR", "artifact": "7856083604" }, { "job": 83248032002, "time": 313, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7856101896" }, { "job": 83248031969, "time": 153, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7856040087" }, { "job": 83248031967, "time": 365, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7856126119" }, { "job": 83248031934, "time": 242, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7856074335" }, { "job": 83248031925, "time": 158, "config": "macos-release-x86_64", "r": "", "check": "FAIL", "artifact": "" }, { "job": 83247233160, "time": 231, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7855976283" }, { "job": 83248032109, "time": 168, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7856043996" }, { "job": 83248031946, "time": 293, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7856094420" }, { "job": 83248031939, "time": 239, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7856072452" }, { "job": 83248031908, "time": 286, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7856090721" } ], "_buildurl": "https://github.com/r-universe/merck/actions/runs/28113599674", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/merck/gsdesign2", "_commit": { "id": "68bc1ff178f6c54a23a5846f276ce52a2eac7d9b", "author": "Yihui Xie ", "committer": "GitHub ", "message": "perf: optimize gs_design_ahr (~3-4x speedup) (#623)\n\n* perf: replace object.size() with numhash() in prune_hash\n\nobject.size() walks the entire hash table structure on every\ncache_fun() call, taking ~2ms per invocation. Since cache_fun is\ncalled 15+ times per gs_design_ahr run (via expected_time/ahr and\ngs_power_npe), this adds up to significant overhead.\n\nReplace with numhash() which returns the entry count in O(1), and\nuse clrhash() for a simple eviction strategy when the limit is\nexceeded.\n\nCo-Authored-By: Claude Opus 4.6 (1M context) \n\n* perf: replace dplyr full_join/select/arrange with base R in gs_design_npe\n\nThe output assembly in gs_design_npe used full_join (to merge H0 and\nH1 probabilities), select, rename, and arrange from dplyr. Since\ngs_design_npe is called once per gs_design_ahr and these operations\nare on small data frames (6 rows), base R merge() and column\nsubsetting are much faster.\n\nCombined with the gs_power_npe change, this yields ~50% overall\nimprovement for multi-analysis designs.\n\nCo-Authored-By: Claude Opus 4.6 (1M context) \n\n* perf: replace dplyr operations with base R in gs_design_ahr output\n\nReplace mutate, full_join, select, arrange, and filter operations\nin the output assembly section of gs_design_ahr with equivalent base\nR operations (direct column assignment, merge, column subsetting,\norder).\n\nThis eliminates the dplyr overhead for the final output formatting\nwhich previously involved multiple tibble round-trips on small data\nframes.\n\nCo-Authored-By: Claude Opus 4.6 (1M context) \n\n* perf: remove dplyr from hot-path functions (expected_time, gs_info_ahr)\n\nReplace select(-n) with base R column removal, and replace\nmutate/transmute in the info_frac loop of gs_design_ahr with\ndirect column assignment. 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"define_enroll_rate" ] }, { "page": "define_fail_rate", "title": "Define failure rate", "topics": [ "define_fail_rate" ] }, { "page": "expected_accrual", "title": "Piecewise constant expected accrual", "topics": [ "expected_accrual" ] }, { "page": "expected_event", "title": "Expected events observed under piecewise exponential model", "topics": [ "expected_event" ] }, { "page": "expected_time", "title": "Predict time at which a targeted event count is achieved", "topics": [ "expected_time" ] }, { "page": "fixed_design", "title": "Fixed design under non-proportional hazards", "topics": [ "fixed_design_ahr", "fixed_design_fh", "fixed_design_lf", "fixed_design_maxcombo", "fixed_design_mb", "fixed_design_milestone", "fixed_design_rd", "fixed_design_rmst" ] }, { "page": "gs_b", "title": "Default boundary generation", "topics": [ "gs_b" ] }, { "page": "gs_bound_summary", "title": "Bound summary table", "topics": [ "gs_bound_summary" ] }, { "page": "gs_cp", "title": "Conditional power computation with non-constant effect size for non-/crossing an upper boundary at analysis j given observed Z value at analysis i", "topics": [ "gs_cp" ] }, { "page": "gs_cp_simple", "title": "Simple conditional power computation with non-constant effect size", "topics": [ "gs_cp_simple" ] }, { "page": "gs_create_arm", "title": "Create npsurvSS arm object", "topics": [ "gs_create_arm" ] }, { "page": "gs_design_ahr", "title": "Calculate sample size and bounds given targeted power and Type I error in group sequential design using average hazard ratio under non-proportional hazards", "topics": [ "gs_design_ahr" ] }, { "page": "gs_design_combo", "title": "Group sequential design using MaxCombo test under non-proportional hazards", "topics": [ "gs_design_combo" ] }, { "page": "gs_power_design_npe", "title": "Group sequential design computation with non-constant effect and information.", "topics": [ "gs_design_npe", "gs_power_design_npe", "gs_power_npe" ] }, { "page": "gs_design_rd", "title": 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non-proportional hazards", "topics": [ "gs_power_combo" ] }, { "page": "gs_power_rd", "title": "Group sequential design power of binary outcome measuring in risk difference", "topics": [ "gs_power_rd" ] }, { "page": "gs_power_wlr", "title": "Group sequential design power using weighted log rank test under non-proportional hazards", "topics": [ "gs_power_wlr" ] }, { "page": "gs_spending_bound", "title": "Derive spending bound for group sequential boundary", "topics": [ "gs_spending_bound" ] }, { "page": "gs_spending_combo", "title": "Derive spending bound for MaxCombo group sequential boundary", "topics": [ "gs_spending_combo" ] }, { "page": "gs_update_ahr", "title": "Group sequential design using average hazard ratio under non-proportional hazards", "topics": [ "gs_update_ahr" ] }, { "page": "ppwe", "title": "Piecewise exponential cumulative distribution function", "topics": [ "ppwe" ] }, { "page": "pw_info", "title": "Average hazard ratio under non-proportional hazards", "topics": [ 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